Purpose: This article explores the question: what shapes young refugees’ often high educational aspirations? A sense of mastery and future hope are among the numerous factors that can positively predict school achievements. High aspirations may thus contribute to both young refugees’ improved educational outcomes and wellbeing.Method: We discuss findings from semi-structured interviews in three Norwegian upper secondary schools in light of theories and research into the often high educational aspirations of immigrant youth.  Results: Several of the study’s findings support the dual frame of reference theory which has been used in prior discussions of migrants’ aspirations. However, we argue that aspirations can be supported not only by comparisons with the country of origin, but also by comparisons with different stages of the refugee experience.Conclusions: The findings suggest that temporal aspects of high aspirations should be empirically and theoretically explored further. Moreover, this and other studies’ findings on young refugees suggest that research into the educational aspirations of young immigrants should address/control for migration category. Upper secondary school provides 3 years of general education, or 4 years of vocational education and training (including 2 years of apprenticeship). Upon finishing lower secondary education, all students have a right to upper secondary education, which nearly all students make use of, including recently arrived language minority students.preparatory class at an upper secondary school. There, they are taught compulsory school subjects, with a special emphasis on learning Norwegian.Young immigrants have a higher dropout rate in upper secondary education than their Norwegian-born peers . As Ferede  states, What may these distinct experiences involve? First, many refugee late arrivals have had little or interrupted education because of the flight and political instability and war in the country of origin  contribute to refugees’ often high aspirations.Furthermore, several studies find that educational aspirations can be motivated by altruism—wanting to benefit others in the country of origin , which is a sub-study of the Nordic research project Coming of Age in Exile (CAGE).The TURIN study’s primary objective is to gain insight into what may promote or inhibit educational and psychosocial transitions among young refugees in the resettlement phase. The study was carried out by researchers at the Norwegian Centre for Violence and Traumatic Stress Studies (NKVTS) and the University of South-Eastern Norway (USN). This article is based on data collected by the NKVTS team at three upper secondary schools in the Greater Oslo Region.The study adopted a qualitative, ethnographically oriented, study design, based on interviews and participant observation in the participating schools. Two researchers, including Pastoor, and a research assistant conducted semi-structured interviews (lasting approximately 45 min to 2 h) with refugee students and school staff at one school each.The main interview guide themes for young refugees included questions on everyday life, school experiences, psychosocial aspects of resettlement, life before arrival in Norway and future plans and aspirations.Based on the acquired consent from the Norwegian Data Inspectorate, the county education authorities were contacted enquiring about whether they were interested in participating in the study, and which schools would be relevant to approach.In the three Greater Oslo region schools, 26 individual interviews were conducted with 26 refugees with 6 years or less residency in Norway. The interviewees were between 16 and 25 years old . The sample includes nine unaccompanied minors, eight individuals family reunified with refugees, four former asylum seekers arriving without family, two former asylum seekers arriving with family, and two UN resettled refugees. The interviewees came from Eritrea (9), Somalia (7), Syria (4), Afghanistan (3), and three other countries.In the three schools, interviews were also conducted with 30 teachers and other school staff. We refer briefly to some relevant parts of this dataset, and otherwise use it as contextual background material.Each participant signed a consent form (approved by the Norwegian Data Inspectorate), after having been informed about the study’s aim, data collection, data management, and their right to withdraw from the study at any time.The article’s findings are primarily based on analysis of interview transcripts. Professional transcribers transcribed the interviews verbatim. All interviews were coded in NVivo, a software program for qualitative and mixed-methods data. The program supports the researcher in organizing and analysing qualitative findings, for example, during the process of moving from the researcher’s initial, descriptive themes towards more abstract, analytic categories. Most of the coding was conducted by Lynnebakke, in close, regular dialogue with Pastoor about the emerging analysis. For closer access to nonverbal cues such as tone of voice and length of hesitations, most of the coding was conducted whilst listening to the recorded interviews. This seemed to reduce the distance to interviewees by obtaining more information about their personalities and aspects of the interview situations, such as the degree of rapport.The coding began with open codes, followed by merging codes in overarching categories and reorganizing the codes into coding trees through several cycles Saldaña, . In addiNotwithstanding our initial resilience lens, the extent to which many young interviewees expressed self-efficacy at school was striking. The “educational resilience” concept thus became a valuable concept brought in at a later stage of analysis, as did theories on immigrant youth’s educational aspirations.The student accounts on motivational reasons outlined in the findings section (third sub-heading) were developed through a process of analytic induction Katz, , entailiAll quotes used in this article are translated from Norwegian. For anonymisation purposes, we have omitted certain details  from the accounts.The interviews varied in length and detail. In some of the interviews, interviewees’ Norwegian language levels were a challenge. This resulted in shorter, often affirmative answers that could not be used in the analysis. The analysis draws primarily on interviews with rich, more elaborate accounts on the article topics in order to report interviewees’ experiences as much as possible 0n their own terms., The relatively formal interview setting in schools may also have influenced some students’ degree of openness. Additionally, some young refugees may not want to disclose personal reflections and experiences due to both common adolescence development processes and healing processes. Strategies for dealing with psychological distress and trauma can vary over time, where talking about distress can seem helpful to healing at some times and disruptive at other times Kohli, . MoreoveTrust often increases over time, and some research has found that young refugees may give surface narrative of themselves in the beginning Hynes, . In thisSome refugees may want to have a self-presentation that conveys resilience and strength as a direct counter-reaction to that that refugees are often generally are conceived through gaze of pity, vulnerability, or charity  a new language but tended to describe these challenges as manageable. Few interviewees reported psychological distress at the time of the interview to the extent of this affecting their schoolwork negatively, such as problems with concentration and/or sleep. However, several reported having experienced strong psychological distress at an earlier stage in Norway, for example, before being granted a residence permit. Some stated that their mental health had improved due to previous help from health professionals.Researchers of unaccompanied minors underscore that vulnerability can coexist with resilience, agency, and resourcefulness  [firmly] Yes … both.//Most refugees I have taught have struggled psychologically. Often, it’s a bit like they’re in an early phase of being in love when they come to Norway, because now they have arrived in a safe country and everything is going to be so good and [they can have] very high expectations and suchlike. And then they learn quite a lot of Norwegian in the beginning, but then they see that things are more complicated [than they expected] and that they need to learn much more. And it wasn’t quite the way they had thought, and then they get a bit burnt out, eh, and it’s like such normal migration problems, and those who in addition have traumas get hit especially hard. Then they can be incapacitated for learning for shorter or longer time periods.Our findings demonstrated that high motivational levels and  resilience are not static individual traits. These findings, again, pointed to the importance of a supportive environment for educational aspirations. Moreover, the findings showed how motivation can vary over time due to the refugee experience. Some interviewees with current high aspirations told about how they previously had been rather indifferent about school in the asylum period, when their future was more unknown. Moreover, teachers reported that it was sometimes difficult to support sustained motivation of recently arrived immigrants, including refugees. Some commented that they had seen many recently arrived students come with high optimism that later decreased when school was more difficult than expected. Several teachers emphasized the interrelation between mastery experiences and motivation, and some commented on how educational achievements can influence mental health and vice versa, as also found in much research  and the refugee experience—highlighting how previous trauma may make it even more demanding to sustain motivation over time.1. Motivated by the aim of an economically secure and stable futureYoung refugees gave diverse reasons for their high educational aspirations. Interviewees were usually motivated by several reasons, but often emphasized one reason. Some reasons may be shared by all youth, whereas other reasons may be shared with other migrant categories. Finally, some reasons reflect the refugee experience specifically. The latter suggests that a quest for understanding migrant youth’s often high aspirations should include consideration of the relevance of migration history.The most commonly stated motivations among student interviewees involved aims for a stable, economically secure future. This included statements about aiming for a specific job ; being independent; “being someone” and not working in vulnerable lower-skilled jobs.2. Motivated by social relations/significant othersSuch aims are shared by many young people, regardless of background. Some interviewees linked their aims for future stability to what they described as Norwegian or universal norms of the importance of working hard to reach one’s goals. On the other hand, several interviewees reported not taking education for granted, which could relate to the refugee experience. We will return to this.3. Motivated by wanting to contribute to societyThis category included statements about being motivated by aims of providing economically for one’s family (in Norway or abroad); being able to pay for family members’ visit to Norway; the encouragement and praise from one’s teacher; being a role model for siblings and being accepted as a potential son-in-law. A few of these explanations relate to the migration experience more broadly.wanting to make a difference in Norway or other countriesusing one’s personal experience to the benefit of othersbeing part of Norwegian societyThis motivation category included statements on:The last bullet point is worth highlighting, since, to our knowledge, it is usually not addressed in migration-related explanations for high aspirations. Tahir is 25-year old and came to Norway as an asylum seeker. He explained:(Int.: Do you feel at home here?) Home? (Int.: Yes?) One has to accept reality. If I say home … Yes, no, well. No, because of the issue with Mahad [media case], have you seen the case? [He is] from Somalia [and after] 17 years [in Norway] suddenly he loses his right to residence.//No, [I don't feel at home] if I think that way. But I am content. I feel like I am part of society because I go to school, I am getting an education, and I work. I pay tax. So I feel a bit positive.  Yes,//I’m young, so I have opportunities and can work, right, instead of being on benefits and saying, ‘I don’t have this, I don’t have that’. I feel a bit, like, positive.4. Motivated by personal developmentTahir was the only interviewee who explicitly stated that he expected labour market discrimination. A broader reading of Tahir’s interview indicated that the quote had an undercurrent of claiming belonging by proving himself a decent citizen  opportunities in the country of origin. Several interviewees reported that school was one of the best things about Norway and some reported appreciation for that school and most higher education is free in Norway. These findings align with the findings of Shakya et al.  You can go to school, but//whether you go to school or not, you don’t get … Anyway, it will be the military, right.//You don’t have freedom, actually.Wanting to give back to the country of residence Making migration worthwhile. Yonas, an unaccompanied refugee minor from Eritrea, wanted to make an extra effort to improve himself and to build something new because of his exile:Many have lived here for 10–15 years and don’t do anything. They just exist//. They haven’t done anything, they haven’t bought an apartment, they haven’t developed—they just live and drink and eat in town.//If I live with my family in Eritrea, that is not a problem, but when one moves and loses one’s family—I must think of myself—why I moved from my family. I must think.Statements in this category included comments on:Yonas’ statement seems to tangent the findings of McWilliams and Bonet , where sThe following two narratives aim to show how key events and reasons can jointly shape educational aspirations in unique individual trajectories. During the presentation, we consider the relevance of the refugee experience for these interviewees’ aspirations.Lilah is a twenty-year-old female who attends preparatory class. She came to Norway with her father as an asylum seeker from Syria two years ago. Later, her mother, siblings, and some other family members were reunited in Norway. Lilah lives with her parents and siblings and enjoys spending time with her family.gender roles. She explains that in Syria, “girls must stay at home, cook for the family and look after smaller siblings” whilst in Norway, girls “always have to go to school and then work.” This highlights the dual frame of reference. Second, Lilah states that it is good to attend school because if family members in Syria call and ask her parents about her, they will be impressed and “happy when they hear that I study, keep working”, since a lot of people in Syria are not able to do this because of the war. On the surface, this could be read as an example of migration as a family project for social mobility, and points in a similar direction to some other research , which personal development, stating that what is best with school is not only learning subjects, but also that she can learn from her mistakes. Fourth, Lilah states that she wants to attend upper secondary school because that is common in Norway. Finally, she is motivated by aims of having economic security and freedom, explaining that she wants to “get a job, earn money, buy an apartment, have a large family” and being able to afford to visit family abroad. Her high aspirations thus reflect both the refugee experience and many youth’s aims of economic independence.Third, Lilah is motivated by Salam is a 23-year-old male from a Middle Eastern country who came to Norway as an asylum seeker four years ago. After leaving his war-torn country of origin, he worked and lived in two other countries. Salam’s flight was long and turbulent; however, he also had several experiences of being helped out of difficult and dramatic situations during the flight.I have never said no to school,//I would never have been like ‘no, now I’m not going to school.’ When they said “you have an offer”, I said “ok, let’s go to school”. Because I have worked too much under people, for example, as a slave. I have always worked as a slave in other countries.//When you understand something, you are the boss, right. When you don’t understand anything, you just have to work. Right. So I always thought about becoming a boss one day, right.//So I started going to school.Salam had no schooling until he was a teenager. However, he is good at learning new languages, which seems to have helped him in Norway. Except for one subject, he reports not finding school especially difficult. When he was waiting to be granted asylum/residency, he had concentration problems, but this did not trouble him later. Despite lack of prior schooling—or perhaps because of it—Salam was focused on getting an education even before he came to Norway. His narrative of the flight through different European countries reads as an account where his agency for education and a safe, stable future intermingled with coincidences and information he received during the flight. For example, when he told someone that he wanted to get an education, he was advised to seek asylum in another Nordic country, but upon arrival there, he had a chance meeting with another asylum seeker from his country of origin who said Norway “is better”. Together with other coincidences, this led Salam to move to Norway. The morning after his arrival, someone knocked on his door in the transit centre and told him he was going to school. Salam had not expected school so soon but was eager to go. He explains that he has worked hard since he was a child, often under harsh conditions, and that this motivates him to create a different future for himself:Imagine, seven years ago when I came to Norway, I had nothing! I wore clothes I maybe hadn’t washed in a month, right. I slept on the street and suchlike.//Then [it] began. And today I have a car, my house is filled with all I need. Clothes, money, school, education. Health.//Maybe I couldn’t have achieved as much in other countries as in Norway. So, in my thoughts, Norway was the country I needed. And in the future, I [will] try to do my best for this country also, because if I’d stayed in [country of origin] today, I would have been killed, or if I hadn’t been killed, I’d have no education//. In other countries [he has transited through] also.//I will try to complete an education and help and work back for this country, right.Salam’s statement reflects a dual frame of reference where socioeconomic comparisons are central, thus potentially being a sentiment shared by individuals of other migrant categories. Other parts of his account reflect the refugee experience. Salam has worked a great deal in addition to attending school, and been able to buy a car, and has plans to buy an apartment with his wife. He aspires to a higher education profession within health care, motivated by a strong gratitude and desire to give back to Norway.This statement has a strong ring of relief. Whilst Salam has aimed to make a safer and better future for himself, his reasons for high aspirations also changed after migration to also include a sense of gratitude.During adolescence, young people must make fundamental decisions about their future, such as further education and career Rutter, . The prewhat shapes young refugees’ often high educational aspirations? We have accounted for a broad set of potential reasons in our own research and other studies. Our findings showed reasons that reflect the ordinary desires of many young people; reasons that may be shared with other migrant categories and reasons that highlight the refugee experience.This article has explored the question In the introduction, we highlighted that school and education potentially have additional meanings for young refugees because of the demanding refugee experience and the stabilizing role school can play. The findings on the potential relevance of the refugee experience for aspirations suggest gaps in current theories on immigrant youth’s often high educational aspirations. We saw that the refugee experience may influence aspirations and the meaning of education in several ways such as e.g., being motivated to make the flight worthwhile or due to altruism that is fuelled by conditions in the country of origin, as also found in other research on refugees. We also presented how Salam’s gratitude for safety and social mobility opportunities in Norway motivated him to give back. Furthermore, we saw that for some young refugees, being motivated by personal development can be strengthened by a dual frame of reference and a strong sense of gratitude for a “new life”. This also exemplified that individuals can have several motivation reasons and that different reasons can interact.Some of the findings can be conceived as “immigrant optimism”, but the intergenerational project for social mobility was not central in the findings.ethnic explanations were not prominent reasons for high aspirations.The contrast between our findings and other Norwegian studies on the role of parents in educational aspirations may partly reflect the different country backgrounds of the research participants in the studies, as educational aims  can vary greatly among different country backgrounds (e.g., Østberg, In numerous places, we have commented on ways the findings support the dual frame of reference theory. Some of these comparisons highlighted coming from war-torn/unstable settings. The findings suggest that discussions on “immigrant optimism” should take into account temporal aspects of aspirations. Optimism is often not a constant of the refugee experience. Moreover, the findings showed that a dual frame of reference may involve not only comparisons with prior experiences and conditions in the country of origin, but also with different stages of the refugee experience, such as the flight and asylum-seeking process. This is demonstrated by reports on becoming more motivated for school after being granted residency. Furthermore, both Salam’s account of gratitude and the findings from previous studies (McWilliams & Bonet, migration reason should also be theoretically explored and empirically controlled for in surveys, and further explored in qualitative research.Research into the often high educational aspirations of immigrant youth pays due attention to differences within the “immigrant population”, especially socioeconomic and country backgrounds (e.g., Bakken & Hyggen, Moreover, the findings of this and other studies on young refugees bring attention to temporal aspects of the refugee experience and how this may influence aspirations. Whilst research has addressed generational changes in educational aspirations over time (cf. Suarez-Orozco et al.,


The response by the author. I thank Michael Bronstein and Sophia Vinogradov for their interest and comments. I would like to respond to a few of their points.First, I agree with the authors that empirical studies should be conducted to validate any approaches to prevent the spread of misinformation before their implementation. Nonetheless, I think that the ideas I have proposed may be worth further discussion and inspire empirical studies to test their effectiveness.etal, et al put it, “such honesty… is a prerequisite for maintaining a trusting relationship between medical institutions (and practitioners) and the public”  was used as reliable source of information, which raises questions. For instance, Roozenbeek etal ; the information about the laboratory origin of the virus being false is no longer present on the WHO “mythbusters” website (https://www.who.int/emergencies/diseases/novel‐coronavirus‐2019/advice‐for‐public/myth‐busters). Against this backdrop, some results of the study by Roozenbeek etal (etal (Last but not least, I would like to call attention to the importance of how veracity of information is determined in empirical studies on misinformation. For example, in a study of Roozenbeek ek etal  used a sl etal, . Yet, isl etal, . Interesek etal  seem misl (etal  does notI hope that discussion and research on these and related issues will continue.


Various types of neural networks are currently widely used in diverse technical applications, not least because neural networks are known to be able to “generalize.” The latter property raises expectations that they should be able to handle unexpected situations with similar success than humans. Using fundamental examples, we show that in situations for which they have not been trained, artificial approaches tend to run into substantial problems, which highlights a deficit in comparisons to human abilities. For this problem–which seems to have obtained little attention so far–we provide a first analysis, based on simple examples, which exhibits some key features responsible for the difference between human and artificial intelligence. The evolution of the species and how it works remains a domain of vibrant discussion; for a recent overview see, e.g., Smith . As one One puzzling issue raised by the solutions obtained in this way is that they are often much larger than what we would expect from a comparison with corresponding specialized biological networks  − λσ(G) combining the unconstrained fitness function Q(G) of the network G with a cost term constraint σ(G) that measures the size of the network G. λ ∈  is a parameter that, upon its increase, may be biased from essentially fully connected (λ = 0) networks that solve the desired task, to the sparsest networks with the latter property .We first show, using simple examples, that optimization based on the main principles of evolution, may in principle provide the simplest solution structure for neural network solutions. In our example, we use the method of evolutionary optimization on a population of classical one-hidden-layer neural networks of a fixed more-than-sufficient size. We apply a fitness function Heading for a sufficiently simple task, we search for neural networks that implement logical gates, in our case AND XOR, in the most efficient way . For our simulations, we started from a population of 20 fully connected networks with two inputs implementing the two logical input channels, connecting to one hidden layer of 30 neurons, followed by a layer of one output neuron providing the result of the desired logical function.F of the population members. For the next generation, parent networks are cut into two parts each and recomposed toward the new generation. Eventually, mutation was applied, and very small weights were set to zero. Running this standard evolutionary paradigm  with a cost term constraint σ(G) punishing for the size of the network, after a sufficiently long evolution, the minimal logical structure of a problem's solution can be obtained. While this may be highly desirable, in particular from various theoretical points of view, the question of how much this bears relevance for practical applications remains.Our experiments corroborate that by using fitness functions To investigate this issue, we turn to problem solutions generated using the genetic programming paradigm , such as :s, If||||sw],If], If, east],||se),If]&&west&&south)&&If, n]], If, e),!(!(east&&nw)&&south&&east)]]], south, east]]&&south&&||southore, If, w, north]||s, If],Sol2 = If, s], west&&n, west]||(east&&!w)If, w, north]||s, If],If, s], west&&n, west]||(east&&!w),If&&south, !If, n, east]&&east]]&&west, south, east]If&&south, !If, n, east]&&east]]&&west, south, east].If[!If[ne, east, While all of these programs solve the posed problem in a satisfactory manner  on all possible (in our case 212) system states. Comparing the last two solutions, we observe that out of 4,096 possible cases, the solutions differ on 1,216 cases, and an even bigger disagreement can be observed with respect to the first and the latter two solutions . This result points to a substantial problem involved in this paradigm of solution construction. It means that the robot's behavior outside the labyrinth  solutions? The answer has to do with the structurally stable solutions that emerge from evolution . To arrive at the simplest solution, our problems were presented over and over, with no variation. This, however, is not even close to how evolution takes place in the real world. Real-world evolution is known to take place in rigged, ever-changing fitness landscapes, leaving in a species' DNA or, more generally in a “solution,” elements of ancient experience of the interaction with it. The implication that we may draw from this is: the smaller its size and therefore also the clearer its logical structure is, the more ignorant a network will be regarding other issues that it might, occasionally, be confronted with. Reversing the above argument, to be prepared for unexpected challenges, a network thus must be larger than minimally necessary for solving a task, even if it will be engaged mainly in solving this task in the most modest context. One distinction of artificial vs. animal intelligence is thus that in both artificial approaches that we have taken above, we have not included the physical reality that a real-world decision system has been confronted with, and has been shaped by, during evolution. Physical reality is not an arbitrary structure but reflects an “inner connectivity” of the world around us; fitness landscapes do not change in an arbitrary manner, but their changes are constrained to respect the environment's deeper construction rules, such as the laws of physics or chemistry that reflect it. Changes in the fitness landscape occur on time-scales similar to those required in terms of generations for population optimization . Therefore, they must be expected to leave long-term traces in the genetic code, providing to a real-world system guidelines for the encounter with “unexpected” situations that are better than arbitrary ones. For technical applications, the inclusion of corresponding properties into the search of optimal solutions may be feasible and may lead to solutions with an improved robustness for the encounter with unexpected situations.9 base pairs). Much smaller than that of a certain very ancient lungfish protopterus aethiopicus (~ 140 · 109), or of a certain amoeba dubia (~ 670 · 109), or even that of an onion (~ 18 · 109), lilies (~ 90 · 109) or that of Pinus, cf. In a reformulation of our insights for the biological genetic context, we might say that animal (and in particular: human) intelligence seems to be founded on an implementation method similar to the one used by the biological DNA: DNA cannot define every cell of an individual individually but is sufficient to act as the key to a factory offered by the environment . Together, through their interaction they produce the final living system, similar to how initial conditions lead dynamical systems to a final solution. Such an interpretation would be consistent with the observation that the more controlled the environment of a living system is, the smaller its amount of DNA generally is [e.g., the human DNA is, measured in base pairs (~ 3 · 10The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The nature of thermal phenomena is still elusive and sometimes misconstrued. Starting from Lavoisier, who presumed that caloric as a weightless substance is conserved, to Sadi Carnot who erroneously assumed that work is extracted while caloric is conserved, to modern day researchers who argue that thermal energy is an indistinguishable part of internal energy, to the generalization of entropy and challengers of the Second Law of thermodynamics, the relevant thermal concepts are critically discussed here. Original reflections about the nature of thermo-mechanical energy transfer, classical and generalized entropy, exergy, and new entransy concept are reasoned and put in historical and contemporary contexts, with the objective of promoting further constructive debates and hopefully resolve some critical issues within the subtle thermal landscape. There is a need to “shed more light onto dissipative heat”. It is the goal of this treatise to contribute to that aspiration. Richard Feynman once stated, “It is important to realize that in physics today, we have no knowledge what energy is” . This stThere are many puzzling issues surrounding thermodynamics and the nature of heat, including subtle definitions and ambiguous meanings of very fundamental concepts. In modern times, there is a tendency by some authors to unduly discredit thermal energy as being indistinguishable from other internal energy types. Romer  argues tThe nature of heat was intriguing since the introduction of caloric and still is an elusive concept. Lavoisier proposed that “heat is a subtle, weightless substance called caloric”, with the assumption of “conservation of caloric”. The “caloric” was not obviously conserved during dissipative “heat generation” processes, such as drilling, and similarly, so that “caloric theory” has been discredited, regardless of ingenious developments. Caloric was not also conserved in heat engines, as mistakenly assumed by Sadi Carnot . In factSadi Carnot (1824) laid ingenious foundations for the Second Law of thermodynamics and discovery of entropy before the First law of energy conservation was even known , and long before thermodynamic concepts were established in the second half of the nineteenth century. In the historical context, it is hard to comprehend now, how Carnot then, at age 28, ingeniously and thoroughly explained the critical concepts of reversible thermo-mechanical processes and the limits of converting heat to work at inception of the heat engines’ era, when the nature of heat was not fully understood. Note that Carnot erroneously assumed that the same caloric (heat) passes through the engine and extracts (produces) work by lowering its temperature, similar to how the same water flow passes through the water-wheel and produces work by lowering its elevation potential. This error (violating energy conservation), considering the knowledge at the time, in no way diminishes Carnot’s ingenious reasoning and conclusions about limiting, reversible processes and its accurate limitations of heat to work conversion .m = E/c2) through material systems involved, from a mass-energy source to a sink system, as depicted on It is widely believed that thermal heat conduction and mechanical work transfer are “massless” phenomena. However, based on existing observations of atomic electron-shell interactions and well-established phenomena and theories, including Einstein’s mass-energy equivalence  and therDenying the existence of thermal energy is the same as denying the existence of its transfer (heat transfer) ,10. SomeHeat is a unique and universal concept representing energy transfer of thermal random-motion and its interactions, while all other energy transfers are classified as different types of work. The “thermal energy” or “stored heat” represents stored energy of relevant thermal motion and interactions due to thermal heat transfer or heat-generation, i.e., dissipation-conversion of all other energy types to thermal heat. The term “thermal heat” represents here the holistic meaning of both, the heat as transfer of the stored thermal energy and the stored thermal energy itself.There is an important peculiarity about spontaneous heat transfer processes such as within simple heat exchangers (without forced flow work): no heat conversion to work like in heat engine, and no other heat generation from work dissipation, but only the Carnot’s “thermal work-potential dissipation” to heat itself at lower temperature—resulting in the conservation of thermal energy, i.e., conservation of thermal heat ,10,11. JThe stored system heat increases the system’s “thermal energy” and entropy, the former is distinguished from the other internal-energy types . A related manuscript is being updated, to quantify the thermal energy within the system internal energy .What is the underlying nature of “entropy” and why does it always increase? Why is entropy so intriguing and mysterious, unique and universal, as if it is a miraculous property of natural, material systems? How does it encompass and quantify all processes at all natural space and time scales, governed by the Second law of thermodynamics? And many other elusive and debatable issues, as if entropy is among the deepest unresolved mysteries in nature, defying our common sense . EntropyCarnot paved the way for his followers to define and prove that entropy is a state function, a material property conserved in ideal, reversible cycles , that entropy could not be destroyed since it will imply more efficient than ideal cycles , but is always generated  due to dissipation of any and all work potentials to heat, causing generation of entropy in irreversible cycles ; thereby, quantifying all reversible and irreversible processes and providing generalization of the Second law of thermodynamics ,6. Note Entropy is related to thermal motion of a system microstructure, the latter gives rise to all thermal phenomena and related properties, namely, temperature, thermal or heat capacity, thermal energy and entropy, among others, see A demonic being, introduced by Maxwell, to miraculously create thermal non-equilibrium by taking advantage of non-uniform distribution of molecular velocity in equilibrium, and thereby violate the Second law of thermodynamics, has been among the most intriguing and elusive wishful concepts for 150 years now. Maxwell and his followers focused on “effortless gating” a molecule at a time, but overlooked simultaneous interference of other chaotic molecules, while the demon exorcists tried to justify impossible processes with misplaced “compensations” by work of measurements and gate operation, and information storage and memory erasure with entropy generation. It is reasoned phenomenologically and deduced by this author that a Maxwell’s demon operation, against natural forces and without due work effort, is not possible, since it would be against the physics of the chaotic thermal motion, the latter without consistent molecular directional preference for selective timing to be possible . MaxwellThe most crucial fact, that the integral, chaotic and simultaneous interactions of all thermal particles on the MD’s operation, has been overlooked, but focus on a single, opportunistic particle motion is emphasized, as if the other thermal particles would not interfere, see Landauer  and his Exergy, as work potential of a system in non-equilibrium with regard to a so-called, reference “dead-state”, is well defined in classical textbooks and references. Here, some selected challenges will be discussed.A and B) is initially at non-equilibrium (BT < AT). The mutual work-potential (RevW) is fully dissipated with related entropy generation  during the irreversible process leading to mutual equilibrium at ABT = AB,IrrT.Irreversible versus reversible process towards mutual equilibrium is presented on AB,RevT with entropy changes, but without entropy generation:If, during a reversible process, the work-potential is extracted and entropy conserved, it will lead to another mutual equilibrium at A + B, see dashed lines on AB,RevT (Equation (2)), smaller than in spontaneous irreversible case at ABT = AB,IrrT, since the work potential will dissipate within the combined system instead of being extracted out, i.e.,:If the reversible work potential is extracted from the combined system RevW and entropy generation AB,GenS are not related, and thus not dependent on any reference, surrounding dead-state , since the combined system (A + B) is isolated from its surrounding. However, it is capable of producing (extracting) work due to its initial non-equilibrium. That work-potential is completely dissipated (heat generation within) after coming spontaneously (irreversibly) in mutual equilibrium. Actually, the required condition for spontaneous process is the existence of “mutual, non-equilibrium work potential”. The “exergy” is “hypothetical work-potential” if a system reversibly comes to equilibrium by interacting with an arbitrary reference dead-state system . Exergy is useful for comparison, and practical if our systems are coming in equilibrium with such a reference surrounding .Note on  that theBryQ) at finite temperature difference may be considered as reversible at boundary temperature BryT, and that the irreversibility takes place within the system when the boundary heat is received at a lower system temperature SysT, thus resulting in generated heat and the remaining reversible heat at the system level . This may be perplexing, since it depends where the irreversibility takes place , but if properly accounted for, it will result in the same outcome.Note also that boundary heat transfer system B is heated from BT to ABT, instead with system A, but with another system B+ with variable temperature, always infinitesimally higher than A’s, then such a process would be reversible (in limit) and without  entropy generation. The entropy (a system property) is subtle and so is irreversible entropy generation (a process quantity), that it becomes the property after the process is finished.Furthermore, the irreversibility is related to a process, not a system LossW = DissW = GenQ) and entropy generation (GenS) are function of the initial and final process states’ properties only, and not of any other reference dead-state, as it might allude at first, since the combined system (A + B) is isolated from the surrounding, i.e., it comes to mutual equilibrium, and does not interact with the surrounding.The irreversible work loss, i.e., work dissipation to generated heat  and at state 2 would be xE2= Q2(1 o/T− T2). It appears that the exergy difference, xE1x− E2, is a function of oT. However, for reversible cycle, Q2/T2= Q1/T1 , the relevant quantities are correlated, so the above is reduced to:Similarly, for example, with reference to a surrounding dead-state at oT. Therefore, it is unnecessary to use exergy . Furthermore, for isolated processes without interaction with the surrounding, it may be inappropriate, to use exergy difference, since actual work-loss is relative to mutual equilibrium state reached between the two isolated sub/systems, as demonstrated above (there is no oT and oP in the above expressions). It may be the case for all thermal processes with no net-entropic, nor net-volumetric interactions with the surrounding. It requires further discussions and clarifications.This is an interesting and deceptive outcome: the change of exergy of heat is not the function of dead-state temperature Entropy is generated when work potential is lost  into “generated heat transfer” (randomly equi-partitioned) into the thermal energy at given absolute temperature within the space occupied by the system, or when expansion (elastic) work potential is lost . Therefore, heat is thermal energy transfer due to temperature gradient, while work is other than thermal energy transfer due to other energy-potential gradients, such as pressure, elevation, voltage, gravity, electro-magnetic, electro-chemical, etc.The entropy unit is not “exactly the same” as for the specific heat, since entropy increase at constant volume is equal to the thermal energy increase per absolute temperature level (important), as opposed to per temperature difference for specific heat at constant volume. Entropy also increases with volume increase at constant temperature and during adiabatic expansion unless the latter is reversible . Therefore, during reversible change of volume  there is no change of entropy due to change of volume, but only due to boundary heat transfer if any.ev = I·t = EeQ/V), and heat conduction, is introduced from hv = ZtrQ/vhT = E/T = G/T , thus, in principle, defining a new physical quantity, “entransy” [A new property, based on physical analogy between electrical conduction, represented by the Ohm’s law , has been utilized for stored heat as thermal energy within the material system, For the new quantity, entransy different symbols have been used in subsequent publications.Note that designation of “stored heat”  ≈ f(T), i.e.,The entransy has been defined as a state property, as a function of ”stored heat”. Although the real system specific heat is function of temperature, the entransy has been regrettably and unnecessarily restricted for constant specific heat systems. It is suggested that entransy be defined instead, as integral quantity for variable specific heat for incompressible systems and/or constant volume processes G, reversible heat REVQ, entransy dissipation or loss LOSSG, and Carnot work-potential loss LOSSW is presented on Correlations between entransy WG, is also essential to be defined for processes when thermal heat is converted to work, such as in heat engines. The work entransy could be defined using the entransy balance for reversible, Carnot cycle relationship, and considering that there is no entransy loss in an ideal reversible process, i.e., IN = GOUTG or G1W + G= G2 :GW = G1WG, was derived by algebraic manipulation as W = WTG1= (Q1− Q2)T1= Q1(T1− T2). However, this definition is not appropriate since it does not satisfy condition that entransy loss is zero for reversible processes. There is a need for further interpretations of the entransy concept and possible refinements [In another publication , the worinements .ThU ≡ StoredQ, and heat is the thermal energy transfer, Q ≡ Th,transferU, [Starting from Clausius till nowadays, the obvious but in general not quantified thermal energy, is “lumped” into well-quantified internal energy, provided in Thermodynamic data tables. Some (or many) argue that subtle “thermal energy” is not definable, but the “internal thermal energy” is manifested as heat transfer due to temperature difference. It is argued here and elsewhere  that heat (and anything else for that matter) could be transferred only if it exists as stored quantity in kind, in the first place. It is self-evident in caloric processes and quantified by the caloric quantities that the “thermal energy” is stored heat (directly related to the system heat capacity), ransfer, . per se (or any other math or physics “tools” per se used to describe it), but about spontaneous, forced-tendency  of mass-energy redistribution in certain, irreversible direction (process driving force), from higher to lower energy-potential (mass-energy density in space). Spontaneity implies forced-directionality and in turn irreversibility. No spontaneous, irreversible process could ever be completely reversed or undone. For example, the driving force for the life and processes on Earth is the irreversible dissipation of energy from the Sun.The Second law is not about disorder and probability No Hope” for the challengers [It is hard to believe that a serious scientist, who truly comprehends the Second law and its essence, would challenge the concept based on incomplete and elusive facts. Sometimes, highly respected scientists in their fields, do not fully comprehend the essence of the Second law of thermodynamics. The Second law “challengers” need to demonstrate and quantify destruction of entropy to challenge the universal validity of the Second law. It has been reasoned and thus proven that destruction of entropy, i.e., violation of the Second law, is against the forced tendency of natural processes and thus impossible, leaving “llengers . After aA critical treatise of “Entransy concept and controversies” within elusive thermal landscape, has been given recently by this author . RegardlAs the fundamental laws of nature and thermodynamics are expended from simple systems in physics and chemistry, to different space and time scales and to much more complex systems in biology, life and intelligent processes, there are more challenges to be comprehended and understood.


Dear Sir,We read with interest the recent publication by Wei et al. , investiLactobacillus iners—is identified to the species level. Metadata is also lacking about the cases and controls.First, transparency and replicability are key issues—also in microbiome research . Thus, tSecond, it is important to make available results from negative controls at all major steps through analysis. This would increase the confidence in the low abundant microbiota results being a true biological signal and not a false positive signal—e.g. from contamination. Thus, Wei et al. ought to provide microbiome results on the negative controls, e.g. a comparison in terms of sequencing depth and “signature OTUs” in the negative controls compared to samples from especially the low bio-mass sites such as the endometrium and the peritoneal fluid. In fact, more than 50% of the “signature OTUs” in Fig. 2 of the endometrial and peritoneal fluid, respectively, are well-known contaminants from sequenced blank controls , 5.Third, when choosing to report new diagnostic/stratification methods, it is important to clearly state what defines them. Wei et al. stratified samples according to the dominant genus. What was the rationale behind this method? As previously mentioned, no information exists in the publication on taxon annotation. Furthermore, there is no definition as to what determines “a dominant genus”; lastly why did the authors choose seven subtypes?Lactobacilli ascending from the vagina to the upper reproductive tract. That information would have been interesting also in the present publication. Finally, it has been shown that ascending infection is increased in patients with bacterial vaginosis, OR 5.7  [Despite the somewhat unclear nature of these seven subtypes, the authors further elaborate their analysis to report the ratio between those arbitrary seven subtypes as evidence for differences between endometriosis cases and controls. Instead, we would suggest that the authors applied a rigorous analysis, utilizing the raw sequencing data, to compare alpha and beta diversity metrics etc. between endometriosis cases and controls. In their first publication , interes.8–18.3) —could auIn summary—and in contrast to the importance statement made by the authors—we find that the data analysis itself as well as the information level regarding the data analysis in the publication are not sufficient to answer the question whether a real microbiota of the upper genital tract exists and whether it is associated with endometriosis. In our view and taking the current methods description into consideration, the conclusion of the study could also have been that upper genital tract samples were dominated by well-known contaminants which caused the observed difference between endometriosis cases and controls.


There are errors in the Funding statement. The correct Funding statement is as follows: This study was supported by Instituto de Salud Carlos III through funding awarded to author JPM (project “PI13/02757”) . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


The increased interest in analyzing and explaining gender inequalities in tech, media, and academia highlights the need for accurate inference methods to predict a person’s gender from their name. Several such services exist that provide access to large databases of names, often enriched with information from social media profiles, culture-specific rules, and insights from sociolinguistics. We compare and benchmark five name-to-gender inference services by applying them to the classification of a test data set consisting of 7,076 manually labeled names. The compiled names are analyzed and characterized according to their geographical and cultural origin. We define a series of performance metrics to quantify various types of classification errors, and define a parameter tuning procedure to search for optimal values of the services’ free parameters. Finally, we perform benchmarks of all services under study regarding several scenarios where a particular metric is to be optimized. Quantitative measurements and large-scale analyses of social phenomena in relation to gender are gaining significance as tools to uncover areas of gender bias and inequality, and to ultimately foster women’s inclusion and advancement. Algorithms that can infer the gender category from other features have thereby opened up new opportunities to enhance data previously lacking such information. Examples include social media profiles, GitHub contributors, and authors of scientific publications, the analysis of which regarding gender has led to a better understanding of women’s situation in domains such as tech , media , and acaParticularly in the latter case of bibliometric studies, the most reliable piece of information available for guessing a gender is the name string of the author. The standard approach for name-to-gender inference is based upon querying large (and often openly available) name repositories, such as censuses, administration records, and universal or country-specific birth lists. Occasionally, results are refined with name-forming rules for specific cultures or ethnicities. In the attempt to identify the gender of as many names as possible, often a multi-step combination of database queries, insights from sociolinguistics, and manual corrections is performed. This leads to non-transparent processes for gender inference that might be troublesome to reproduce and almost impossible to test, compare, and transfer.Recently, the plethora of self-labeled data arising from social media has been successfully leveraged to improve the accuracy and specificity of methods based on querying compiled lists of names. This input has given rise to a handful of free and paid web services that infer gender from name strings. They usually gather data from manifold sources and profit from a greater degree of diversity, notably in regard to the origin of names, thus becoming a good choice for analyses outside of a national context. Access to such services is typically granted through APIs, turning gender inference on large corpora into a fast, reliable, and cost-effective process. Using them in large-scale analyses is tempting due to their accuracy and availability; nonetheless, some caveats may apply, as the underlying data sources are frequently closed and thus not necessarily reliable nor verifiable.It is perhaps no surprise that, with few exceptions, the majority of research that uses name-based gender assignment does not evaluate the chosen method nor justifies the decision to use particular data sources and inference methodologies. Furthermore, only a handful of studies have attempted to compare different approaches and provide solid groundwork for the choice of a given tool. We intend to fill this gap by presenting a comprehensive benchmark and comparison of several available gender inference services applied to the identification of names stemming from the academic publishing community.Gender API, genderize.io, NameAPI, NamSor and Python package gender-guesser, five popular and commonly used methods for the problem at hand. All services are able to handle an international set of names and are thus singularly valuable for bibliographic analyses. After describing the services broken down by several decision-critical properties, such as data sources, accessibility, and cost, we test each of them on a manually labeled data set consisting of 7,076 names, which we make publicly available to interested researchers working on this topic  on a control group provided by a government office and consisting of over 400 first names. No claims were made as to which one of the services was better, arguing that different purposes may pose different requirements to the services.Despite its importance for estimating the error rate on gender assignments, only a few studies are devoted to comparing different gender inference tools. Face++, and two novel approaches consisting of mixing the predictions of the latter two. They reported improved accuracy of the mixed methods, noting that the quality of the assignments depended on country and was worse for non-Western regions. The brevity of the paper prevents an extended discussion regarding the definition of the quality metrics, particularly in the handling of the predicted unknowns. At any rate, face recognition techniques clearly hold potential, albeit they must be used with caution in view of their likely intrinsic bias, e.g., towards darker-skinned females , a gender inference service launched in 2014, offers a standard first name search with capability to handle double names. Furthermore, the API allows queries with a full name, which is internally split into first and last. The service currently supports 178 countries, although it cannot geo-localize a full name per se. Its API accepts extra parameters for localized queries though, namely country code, IP address, and browser locale. The response contains gender assignments male, female, or unknown, plus confidence parameters samples and accuracy. The former is the number of database records matching the request, while the latter determines the reliability of the assignment. The service is built upon a combination of data from multiple sources, partially from publicly available governmental records combined with data crawled from social networks. Each name has to be verified by different sources to be incorporated. The service is overall reliable, with its cloud-based infrastructure providing an availability of 99.9%.Gender API (https://pypi.python.org/pypi/gender-guesser/) implements a wrapper around the names data described in Michael (2008)1Genderator and SexMachine, but they are mostly unsupported and not Python3-compatible.Other Python wrappers of the same data are The data set comprises over 45,000 names with gender assignments unknown (name not found), andy (androgynous), male, female, mostly_male, or mostly_female. Additionally, the approximate frequency of each name per country is provided, and the gender request can be made with an extra location parameter for better accuracy. The dictionary of names was published a decade ago and has not been updated since, which limits the usefulness of both the package and its underlying data source. On the other hand, the gender assignment of this collection is presumed to be of high quality, with manual checks by native speakers of various countries.Python package gender-guesser (https://genderize.io/), created in August 2013, attempts to infer the gender of a first name. The response is either male, female, or None, plus two additional confidence parameters, count and probability, representing the number of data entries used to calculate the response and the proportion of names with the gender returned in the response. The underlying data is collected from social networks across 79 countries and 89 languages. Although the service does not geo-localize names automatically, it does accept two optional parameters, location_id and language_id, for more qualified guesses. The providers do not state the sources employed, hence the reliability of the data is difficult to assess. An API and extensions to various languages are available. There are no guarantees about uptime; the service might not be reliable enough for use in critical applications.Online service genderize.io (https://www.nameapi.org/) is a free and paid service platform to work with names. It provides functionality in the form of web services to do name parsing, genderizing, matching, formatting, and others. For our benchmark we have concentrated on the genderizing service only. Its underlying data sources are dictionaries with all parts of names extracted from census publications, birth lists, and telephone books from over 55 countries. Original spellings in non-Latin scripts (including transcriptions and transliterations) are also recorded. The gender response can be MALE, FEMALE, NEUTRAL, UNKNOWN, or INDETERMINABLE, weighted by the confidence parameter. The service is able to infer the most likely origin of the name, thus allowing to apply language-specific rules and to geo-localize names whose gender depend on the culture. The service aims to achieve high uptime, with 99.999% availability. NameAPI provides an API accessible either from a free or a paid account, with credits that can be purchased on a one-time or monthly subscription basis.NameAPI (http://www.namsor.com/) is a classifier of personal names by gender, country of origin, or ethnicity. The service is able to recognize the linguistic or cultural origin of names, thus allowing it to correctly infer the gender from the first and last name in cases that can be male or female depending on their provenience. NamSor claims to cover all languages, alphabets, countries, and regions. The underlying data consists of 1.3 million unique given names extracted from baby name statistics per country, plus sociolinguistic information  to extract semantics, which allows it to predict gender for complex cases. The NamSor gender API returns male, female, or unknown values, plus a parameter scale ranging from −1 to +1 to reflect the certainty that a name is male or female, respectively. A basic API for structured names is available for free, whereas the Freemium version accepts unstructured strings and offers higher performance and precision. NamSor’s origin API recognizes the likely cultural provenience of personal names in any alphabet, returning a primary and an alternative potential country of origin, as well as a score to qualify the trustworthiness of the assignment. It is based on a proprietary onomastics model which uses linguistic and cultural information.NamSor , sampled in 2014 ignoring names that contained only initials. These authors were manually labeled as ‘female’, ‘male’ or ‘unknown’ by zbmath data set consists of 400 names .Randomly selected authors from the bibliographical records of the mathematical publications service zbMATH , authorships and titles that correspond to records of articles of biographical-items or items-about-individual type, respectively, from all fields of study, published from 1945 to 2014 and drawn from WoS. As described in nltk’s POS tagger to identify names in the articles’ titles. Generally, NER tasks are better solved with more potent packages, such as Stanford’s CoreNLP. In this case though, the small size of the data set allowed for a manual check of all extracted names to guarantee correctness. After revising both sources, genderize_r_titles and genderize_r_authors, the data set consists of 1,037 names .Sample data sets from the genderizeR package .Data set from wos data set consists of 3,687 names .Data set produced for the validation study reported in After concatenating all data sets we ran a sanity check consisting of finding names that had been consistently misclassified by all gender inference services, and performed manual corrections to amend incorrectly assigned labels. In total, we double-checked 74 author names and manually changed the gender label of 46 of them by searching preferably for personal pronouns, gender-indicating titles, and, ultimately, photos in university websites, professional social media sites, conference web pages, and similar sources. All undertaken preprocessing steps can be found in Our final data set consists of 7,076 names , split into three components: first, middle, and last name. About 13% of them contain a middle name, and the number of unique combinations of first and middle name in the data set is 3,956. The most common male names are ‘John’, ‘David’, and ‘Michael’; the most frequent female ones are ‘Susan’, ’Christine’, ‘Laura’, and ‘Anne’. We point out that we use ‘female’ and ‘male’ as categories solely because the evaluated services operate under the gender binary paradigm; our concerns to this approach are spelled out in the discussion. To the best of our knowledge this collection is the largest sample of labeled gender data corresponding to authors of academic publications to date. In order to promote further research on the topic, we make our data set available to interested researchers .score that calibrates its reliability. According to NamSor’s internal evaluations, for names from Europe, Asia, and Africa the classifications with score > 0 are trustworthy. Note that the USA is not considered an onomastic class on its own but rather a melting pot of other ‘cultural origins’ such as Ireland or Germany  European descent. The assembled data set described above does not include any information regarding the origin or geographic provenience of the persons’ names. It is well known that the cultural context is an important aspect affecting the reliability of gender inference methods. We thus seek to evaluate the geographical and cultural diversity of our data set as well as to measure the impact of the names’ origins on the performance of the surveyed services. As described above, NamSor’s origin API is able to produce an anthroponomical classification of a personal name by allocating an onomastic class, i.e., a country, subregion, and region, to it. The inferred origin is reported in conjunction with a  Germany 2. 2NamSoscore above 0, and that we keep for further analysis. The service was queried in May 2018. NamSor estimates 4,228 names (61%) to be of European origin, 2,304 (34%) of Asian, and 334 (5%) of African provenience. We have split the analysis by the different data sources; results are displayed in We have applied NamSor’s origin API to our collection of 7,076 names and are able to assign a provenience to 97%  of them that return a A more fine-grained geographical analysis focusing on countries, rather than regions, reveals that the names in our test data originate from 113 countries; however, just 16 of them already cover 75% of the whole data set. The most frequent country of origin is the UK, followed by Germany, China, and Ireland. Splitting the analysis among the five data sources confirms that both genderize_r datasets are very Eurocentric: they have almost no Asian countries among the top 10 most frequent ones. They also show the lowest diversity, with the top three countries  covering 50% of all names and the top 10 covering 75%. On the contrary, the highest variability appears in the smallest data set zbmath, where the top three countries  contain 26% of all names and the top 10 cover 60%. The larger wos data set exhibits the best representation of Asian origins: China, Japan, and Korea appear in positions 1st, 3rd, and 6th in terms of frequency. Full figures and statistics can be found in our dedicated Jupyter notebook in The analysis of cultural and geographical provenience of the personal names contained in our data set brings us to the conclusion that our collection is reasonably diverse and shows an acceptable variability with respect to origin, with the aforementioned caveats about African names.We have performed systematic queries to the services under study to obtain an inferred gender for all records in our test data set. All queries were performed in mid December 2017. Depending on their peculiarities, we sent different requests to the various APIs. Concretely, genderize.io and gender-guesser do not handle last names, therefore we queried using first and middle names only. When both a first and a middle name were present we tried their combinations . If no response was obtained, only the first name was used . The free NamSor API requires the name to be structured as split forename(s) and last name. Gender API offers two distinct end points, one for forename(s) only and another capable of splitting unstructured full name strings. We evaluated both methods in our benchmark and found that the former performs notably better, thus we report results for that variant. NameAPI accepts unstructured strings per se. We tested it using the full name string and the forename(s) only, for better comparison with the name splitting mechanism of Gender API. However, in this case querying with the full name string achieves significantly better results, hence we report the performance of NameAPI for this variant.Name-based gender inference can be considered as a classification problem, and as such there exist plenty of metrics to measure the performance of a classifier. The choice of performance indicators needs to be suitable for the problem at hand and is usually a non-trivial task in the development of data-based algorithms. For further background readings related to classification and learning algorithms in general, such as training and testing, cross-validation, typical error estimates and statistical significance testing, see e.g., The names in our evaluation data set have been manually labeled as ‘female’, ‘male’, or ‘unknown’. Recall that those labeled as ‘unknown’ refer to individuals for whom it was not possible to find sufficient gender-related information online. Therefore the class ‘unknown’ is rather a heterogeneous label applied to people with either very common names or those that avoid providing much personal information online. In particular, it is not a ‘gender class’ in any suitable sense, and cannot be included appropriately in quantitative evaluations using performance metrics. In what follows, we will not make use of the items with manual label ‘unknown’ for any of our calculations and results, working instead with the 5,779 names in our data set which possess a defined gender label.  On the other hand, the services evaluated here do return, along with the responses ‘female’ and ‘male’, at least a label ‘unknown’ for the unidentified cases. Hence, in terms of the true labels we are dealing with a binary classification problem, while the predictions contain one or more extra output classes.This makes it difficult to pose name-based inference as a standard classification problem and to utilize commonly used metrics such as precision and recall. In our case it makes sense to work instead with metrics derived from the confusion matrix defined as follows:assignments: elements in the diagonal (mm and ff) are the correct classifications, while elements outside it (mf and fm) are thus misclassifications. The sum of both can be simply referred to as classifications. Consequently, elements mu and fu represent non-classifications, since the algorithm fails at predicting one of the classes ‘male’ or ‘female’. All mistakes, both misclassifications and non-classifications, are included under the term inaccuracies. Based on the confusion matrix, Let us introduce the following nomenclature for the components of the confusion matrix, which in general we will refer to as errorCoded treats a non-classification as a regular error and penalizes it in the same way as a misclassification, therefore it encodes the fraction of inaccuracies over the total number of assignments; errorCodedWithoutNA measures the share of misclassifications over the total number of classifications while ignoring non-classifications; naCoded computes the proportion of non-classifications over the total number of assignments; errorGenderBias estimates the direction of the bias in gender prediction, indicating whether there are more females misclassified as male, or vice versa. If positive, then the estimated number of women is higher than in the real data.The errors above are to be interpreted as follows: errorCodedWithoutNA should be minimized. For most purposes, however, it is desirable to infer the gender for as many names as possible without treating non-classifications as a regular error. For this purpose we have defined two extensions of the metrics above.Depending on the concrete usage of an algorithm, these metrics can be suitable or not. For instance, if not being able to assign a gender to a large number of names is acceptable while high prediction accuracy is essential, w ∈ . We define the weightedError as Let w = 0 the weightedError equals errorCodedWithoutNA and for w = 1 we recover errorCoded exactly. For 0 < w < 1 we define a metric which penalizes misclassifications more than non-classifications. To clarify this further, consider the following examples of confusion matrices:For C1 exhibits a smaller proportion of misclassifications, given that the number of non-classifications is larger than in C2:For both confusion matrices the fraction of inaccuracies over all assignments is the same , while errorCodedWithoutNA, which ignores the class ‘unknown’, while enforcing a constraint on naCoded, i.e., the rate of non-classifications. Indeed any two metrics can be combined in this way. However, a disadvantage of this approach is that, for certain constraint values, there is no solution in the parameter space of a given gender inference model. Thus, it makes sense to consider the distribution of error values depending on the tuning parameters before setting a definite constraint value. In our benchmarks, if the constraint cannot be satisfied on a training set, we have fixed the test set error to 1, which is the maximum achievable value.Another even simpler possibility to penalize non-classifications without giving them the same importance as to misclassifications is to minimize a metric such as Prior to parameter tuning and benchmarking we have performed sample requests to all services in order to test potentially problematic cases, such as double names and diacritics. All services under study are sensitive towards accents and return different responses when e.g., applied to the names ‘José María’ and ‘José Maria’. However, NamSor and NameAPI show less sensitivity than Gender API and genderize.io. For instance, NameAPI returns the same value of the free parameter for both ‘Jose’/‘José’ and ‘Maria’/‘María’, respectively, but makes a difference when queried with a double name.count value resulting of adding those for ‘Mary’ and ‘Jane’. This pattern persists when concatenating further names of the same gender, e.g., as in ‘Mary-Jane-Sarah’. Yet when name parts are joined with empty space instead of hyphen, the count values are not added, which shows that a different logic is being applied. The response of genderize.io also depends on the character connecting the name parts. This indicates a low level of semantical preprocessing of the data sources for both services. We have found examples of similar behavior in NamSor, while NameAPI seems to be less susceptible to this kind of artifacts.The handling of double names is actually not completely transparent for most of the services. In the cases of ‘Mary-Jane’ or ‘Jane-Mary’, Gender API returns a As pointed out in mostly.Both NameAPI and NamSor make use of the surname in order to provide a more accurate gender guess for names which depend significantly on the cultural context. We tested this on a few examples such as ‘Andrea Schmidt’ vs. ‘Andrea Bocelli’ or ‘Rosario González’ vs. ‘Rosario Giordano’; the two services responded with the expected gender categories, showing a correct identification of the surname with a particular country of origin. Gender API and genderize.io do assign a gender to names like ‘Andrea’ or ‘Rosario’, but with lower accuracy values. When queried with unisex first names such as ‘Mika’, ‘Addison’, ‘Ash’, or ’Dakota’, NameAPI returns ‘neutral’ or ‘unknown’. NamSor, Gender API, and genderize.io interpret some of these names as gender neutral by assigning a value of their free parameter close to 0, while gender-guesser also treats names as ambiguous via the qualifier confidence exhibits a bimodal distribution that peaks at around 0.90, with a secondary maximum at 1 (absolute certainty on the gender assignment). All names reach a confidence of at least 0.70. scale parameter. samples and count, respectively), and the other shows the confidence on its gender assignment (named accuracy and probability). Most names fall in the bottom right region of high confidence and low number of counts.All methods under study with exception of gender-guesser return, in addition to the gender, one or two numerical parameters to estimate the quality of the inference. In general, there is no mathematical model that can be trained in the classical sense of machine learning. Instead, for every service, we have ‘trained’ (or ‘tuned’) the algorithms by trying out randomly sampled parameter values. Assuming that a gender classification might not be reliable under some given threshold for the confidence indicators, we have searched for those parameter values that minimize a certain error. A particular instantiation of a random grid of sampled parameters used for each service (except gender-guesser) is displayed as black dots in We define a series of benchmarks to compare the methods under study. To begin with, we compute performance metrics using the default gender responses for all services, i.e., with no error minimization through parameter tuning (Benchmark 1). Next, we introduce further Benchmarks 2, 3, and 4, each of which concentrates on a particular scenario defined by conditions on the metrics to be optimized.All performed benchmarks are evaluated two-fold: (a) on the entire data set, and (b) differentiating between the five data sources as described in ‘Assemblage of test data’. The latter case is particularly relevant e.g., for researchers in scientometrics looking for the most suitable gender inference algorithm for a particular data source such as PubMed, whereas the former is most appropriate when analyzing different data collections, as e.g., in For Benchmarks 2a, 3a, and 4a we run 10 trials of 10-fold cross-validation. In each, we randomly select (at most) 40 parameter values per service for training, and record the average error on the test sets with those parameters that minimized the respective error on the training sets. The tuning parameters as well as the training/test set splits are randomly selected with fixed seed numbers, thus ensuring reproducibility.In Benchmarks 2b, 3b, and 4b we perform one trial of 10-fold cross-validation per service and data set, and record the average error on the test sets. Since the individual data sets differ significantly in size, we have used at most 20 parameters for the smaller data sets zbmath, genderize_r_authors and genderize_r_titles. For the larger data sets pubmed and wos, we have allowed at most 30 and 35 tuning parameters, respectively.Finally, we apply several tests to assess the statistical significance of the observed differences. In the b-versions of our benchmarks, we first apply the non-parametric Friedman test and then suitable post-hoc tests. We refrain from using ANOVA, which is the usually recommended parametric alternative to evaluate multiple classifiers on multiple data sets, since the homogeneous covariance assumption is not satisfied across all services and data sets. In each benchmark, we compare the two best performers using suitable parametric or non-parametric tests for two classifiers. For more details on statistical significance tests suited for classification algorithms, see The code for the benchmark evaluations can be found in First, we use the gender inference services employing their default responses, i.e., considering all ‘female’ and ‘male’ gender attributions regardless of the value of the confidence parameters.We report the resulting figures for correct classifications, misclassifications, and non-classifications per service on the entire data set in w = 0.2, all services perform quite similarly, with Gender API producing the lowest error.Regarding the metrics that ignore predicted ‘unknowns’ altogether, Python package gender-guesser achieves the best results, with only 2.6% of misclassifications, followed by NameAPI. This means that, when considering only the proper classifications in the confusion matrix, these services minimize the number of elements outside the diagonal. In other words, they produce the least number of misclassifications when ignoring the non-classifications. This is indicative of a high-quality data curation when incorporating names into the database. Regarding the error in gender bias, the worst offenders are genderize.io and Gender API, although in reverse directions; while the former wrongly identifies more men as women, the latter does the opposite, which means that results obtained with Gender API may underestimate the amount of females. Finally, measured in terms of the weighted error with accuracy by Gender API, 0.98 confidence by NameAPI, and 0.92 probability by genderize.io, while NamSor more conservatively sets its scale to 0.16. In fact, our test data set includes a male ‘Dana’. Similarly ‘Michal’, the name of a female researcher, is classified as male with 97% accuracy by Gender API, 0.93 confidence by NameAPI, -0.91 scale by NamSor, and 0.75 probability by genderize.io. Ultimately it all comes down to internal heuristics and the way each service weights the counts for a particular name from their multiple data sources. Thus it is unavoidable to end up with a number of misclassified names, and this should be taken into account when making absolute claims about the validity of results based on these services.It is perhaps necessary to point out that, despite the high accuracy level of various gender predictions according to the services’ responses, some persons indeed remain misclassified. We can provide several examples extracted from our analysis: ‘Dana’ is thought to be female with 91% Next, we investigate the impact of the names’ origin on the performance of the services under evaluation. As described above, all services return a confidence parameter indicating the trustworthiness of the classification; recall that for Python package gender-guesser we have created one such parameter by setting it to 0.75 for responses ‘mostly_female’ or ‘mostly_male’ and 1 for ‘female’ or ‘male’. We investigate the confidence parameters for different geographical origins of the test names. The boxplots in confidence parameter is not as useful to discriminate among easy versus complex cases. It is also worth noting in Note that all services produce responses that are discernibly dependent on the names’ origin: the most confident gender predictions are for names of European origin, while Asian names generally lead to a lower median and a higher deviation. The service NameAPI displayed in confidence and genderize.io’s probability parameters are sufficiently significant.The lower confidence in gender assignments for Asian names reported by almost all services suggests focusing on that case. As shown in errorCoded, however its performance is strongly affected by the names’ origin, being one order of magnitude worse for Asian (18% inaccuracies) than for European names (3%). NamSor performs similarly for European (3%) and African names (7%), but is considerably worse for Asian ones (35%). Regarding the fraction of misclassifications (errorCodedWithoutNA) we note that gender-guesser, with its small but highly accurate database, achieves a mere 1.5% error in classifying European names, while for Asian names the figure increases to 6%. Generally speaking, we conclude that all services clearly show the challenging nature of inferring gender for Asian names.Next let us consider errors in gender inference depending on the source of the names as displayed in errorCoded) under the constraint that at most 5% of all successfully classified names are misclassified as female or male, i.e., they fulfill errorCodedWithoutNA < 0.05. This is a realistic constellation for applications requiring the rate of misclassifications not to exceed a given threshold.We measure the performance of all services with respect to the fraction of inaccuracies  no difference between their variances. The t-test returns a very small p-value <0.01, while the absolute value of Cohen’s d-statistic is 19.1. This means that Gender API’s error is statistically significantly lower than NamSor’s and the effect size is large.In order to assess whether the difference in performance is statistically significant, we apply a two-matched-samples accuracy and samples are 57 and 62, respectively, while NamSor’s scale is tuned to 0.13.The parameter tuning procedure on the entire data set for this benchmark leads to optimal parameters close to the default case, maximizing the number of names that are assigned a gender; for instance, the optimal values for Gender API’s For the split analyses we perform one run of 10-fold cross-validation per service and data source; results are displayed in We have applied the Friedman test with the result that the difference in performance among services is statistically significant at significance level 0.01. As a post-hoc test we have applied the Nemenyi test in order to find out which classifiers actually differ. However, the Nemenyi test is not powerful enough to detect statistical significance between any classifiers except Gender API and NameAPI. Since we are particularly interested in the best performers, we have compared Gender API and NamSor using the sign test instead, which counts the number of data sets on which the one classifier outperforms the other. Accordingly, Gender API is significantly better than NamSor at the confidence level 0.05.errorCodedWithoutNA) constrained to the amount of names that cannot be classified being lower than 25% of all assignments (naCoded < 0.25). This represents a rather frequent situation of wanting to incur as few misclassifications as possible, while at the same time being able to assign a gender to at least a pre-defined fraction of the evaluated names.Next we evaluate the effectiveness for achieving correct classifications, i.e., we minimize the rate of misclassifications .As shown in t-test cannot be applied to measure the difference between the two best performers3We have applied the sign test to the test errors in the first trial, since multiple trials violate the independent domain assumption. Gender API outperforms NamSor in seven of 10 folds but this is not significant in terms of the sign test which would require eight folds.However, it can be applied to the comparison of Gender API and genderize.io, with the result that Gender API outperforms genderize.io significantly with large effect size.Since the variances of Gender API and NamSor are not similar, a For most of the analyzed data sources Gender API outperforms all other services; error figures are displayed in As in Benchmark 2b, the Friedman test shows statistical significance at significance level 0.01. Neither the Nemenyi test nor the sign test confirm significance between the performance of Gender API and NamSor at level 0.05. We conclude that none of the tests considered suitable for comparing Gender API and NamSor are able to confirm that Gender API is statistically significantly better in this case.w = 0.2, namely the metric that treats all inaccuracies (misclassifications and non-classifications) as errors, but puts five times more weight into the former. This corresponds to an intermediate situation between Benchmarks 1 and 2, and the approach has the flexibility of allowing a continuous range of values for the weight w, depending on the particular needs of each analysis.Finally we analyze the case of minimizing the weighted error with t-test, which yields statistical significance at significance level 0.01. Also, Cohen’s d-statistic confirms that the difference in performance is practically relevant.The best results are achieved by Gender API and NamSor with weighted error values of 0.046 and 0.056, respectively, as shown in accuracy and samples are 75 and 72,003, respectively, while NamSor’s scale is tuned to 0.41.As expected, the parameter tuning procedure on the entire data set leads to optimal parameters between those computed for the previous two benchmarks; for instance, the optimal values for Gender API’s weightedError with weight w = 0.2 for all services and data sources. Gender API is the best performing service on all data sets; NamSor reaches the second best values on four out of five data sources. The Friedman test shows that the performances are statistically significant at significance level of 0.01. Furthermore, the sign test shows that Gender API is significantly better than NamSor at significance level 0.05.In Name-based gender inference poses numerous challenges. To name a few, the association of a name with gender depends on the cultural and regional context, hence relying on the first name only can be highly error-prone. Transliteration from other alphabets into the Latin one is known to lead to significant losses of information, thereby excluding entire populations from a reliable classification. Incidentally, the gender of some names might depend not only on culture, but also on historical epoch, and so there exist names that were e.g., typically male in the past and are nowadays female or unisex.Furthermore, first names are per se embedded into the gender binary, hence this approach reinforces a non-inclusive gender concept and further marginalizes individuals that do not identify as women or men. Clearly, the best way to enrich personal data with this type of demographic information is to ask for self-identification. That would not only increase the correctness of the data; it is also to be preferred under ethical considerations, since it avoids the offensiveness of assigning categories to individuals, while allowing for inclusion of identities beyond the gender binary. Self-identification is not feasible though in large-scale studies of historical data that are typical for bibliometric analyses. Thus the usage of automated methods to infer gender from names or from alternative available details is unavoidable. For a thorough discussion of the ethics of gender identification, see Notwithstanding the above caveats, we have performed a comprehensive comparison of available gender inference tools, testing five services on a manually labeled data set containing 7,076 names, of which 5,779 have a definite gender and are subjected to our analyses. For our evaluations, it would have been desirable to use an open collection of names with labels obtained through self-identification. We are not aware of such a set, thus we have used data based on judgments of third parties. As described in the section ‘Assemblage of test data’ we have corrected a non-trivial amount of gender assignments, which unfortunately does not preclude potential remaining classification mistakes. Making the test data set public might help to correct them. Furthermore, we have assessed the geographical diversity of our test names, concluding that approximately half of them are of European origin, slightly less than half are Asian, and the remaining 5% are African. Names of persons from the American and Australian continents are considered to descend from these three main regions. We deem this distribution to be appropriate for the task at hand.We have devised and run various benchmarks to compare all five inference services in several scenarios. In particular, we have computed all performance metrics using the default responses without any further tuning. We have studied the default responses broken down by geographical origin of names and by data source. Additionally, we have performed parameter tuning to search for the optimal values of the confidence indicators that lead to minimization of misclassification or inaccuracy rates, while constraining the maximum error on the other quantity. We have broken down these analyses by data source as well. Finally, we have applied various tests to measure whether the observed differences in performance are statistically significant.Python package gender-guesser achieves the lowest misclassification rate without parameter tuning for the entire data set, introducing also the smallest gender bias. At the same time it shows poor performance in terms of non-classifications, which is understandable given its comparatively small data base. As the only completely free service with open data and logic, we reckon that it can be useful as a first step of a multi-stage gender inference procedure. Gender API is the best performer in terms of fraction of inaccuracies, and also in proportion of non-classifications.When breaking down results without parameter tuning by names’ origin we find out that all services perform at least one order of magnitude better on names of European origin than on Asian names. In particular, this translates to poorer results on the wos names subset, which is the less Eurocentric collection of all analyzed data sources. This confirms that assessments of errors in gender inference studies should be made with particular care when the cultural makeup of the analyzed names is unknown. For instance, the genderizeR data subsets employed in the analysis of Gender API typically achieves the best results after performing parameter tuning to optimize for particular scenarios. It is noteworthy to recall that, in contrast to NamSor and NameAPI, Gender API uses first names only. Using the tuned parameters of Gender API, it is possible to obtain a rate of inaccuracies of 8.7% constrained to not more of 5% of names being misclassified, a result significantly better than that achieved by the second best service NamSor. Likewise, the misclassification error can be made as low as 0.9% while still retaining a classification label for at least 75% of the entire data set. Next in performance is service NamSor, closely followed by genderize.io, both of which achieve a misclassification rate under 2% in that latter scenario. Our results indicate that analyses based on gender predictions by these methods are to be considered as more reliable than regular queries to country censuses or birth name lists.The addition of further benchmark settings based on supplementary performance metrics might be of interest. For instance, an appropriate measure would be the area under the ROC curve (AUC), which is particularly useful when one of the outcome classes is skewed, as is expected for authorships randomly drawn from databases in STEM disciplines.4Considering the problems arising with middle names, as described for Gender API and NamSor, it might make sense to drop them, or to query with only the first name when the genders of first and middle are in disagreement.To give another example of trivial errors, NameAPI classifies ‘paul pinsky’ as female with confidence 0.99, while ‘Paul Pinsky’ or ‘Paul pinsky’ are returned as male with confidence 0.91. Hence, we recommend potential users to thoroughly test any given service with comprehensive examples before going into production. Our benchmarks and tests aim to provide first solid evidence of the services’ capabilities.A disadvantage of the commercial services though is the lack of transparency regarding their data sources, specifically how records are gathered and processed. Furthermore, the algorithms behind the gender assignments are closed, too, while explanations are usually provided only on the level of technical usage, based on simple examples such as ‘John Smith’. Both aspects hamper efforts towards reproducibility of results. At the same time, given the substantial cost of some of the services, a better treatment of specific peculiarities like double names would be expectedgender by Lincoln Mullen  names from Russia or Poland. For those services that can handle them, it would be interesting to benchmark on a test set consisting of surnames only. Additionally, our data set contains only names in Latin characters although various services can handle (some) non-Latin alphabets, so it might be desirable to extend the data set in this direction as well. Furthermore, one could expand the study to include more samples of the same first name and test the dependency of gender inference on the last name. Lastly, there exist several more code packages or web services of interest: R package n Mullen  utilizesThe determination of a person’ gender based solely on their name is not straightforward, yet it is a relevant task for plenty of applications, including but not limited to studies of women’s representation in tech, media, or academia. In particular, bibliometric analyses of scientific publications in various academic fields have mostly made use of compiled lists of names to label authors of articles as male or female. Less attention has been paid, though, to the quantification of the several errors that one can incur when doing so, or to the advantages of choosing one or another gender assignment method depending on the requirements of the analysis at hand.Our comparison of five gender inference services in terms of various performance metrics such as inaccuracy, misclassification, and non-classification error rates provides a solid estimation of the accuracy to be expected in name-to-gender inference tasks. Applying these metrics to our data set, which we break down according to the names’ geographical origin and data source, we estimate the errors incurred by the services according to the two variables. By performing cross-validated, randomized parameter tuning on a large genderized data set of scientific authors we demonstrate that with three of the surveyed services it is possible to guess the correct gender of more than 98% of names for which a female or male label is returned, while simultaneously leaving less than 25% of records unclassified. Our framework can be trivially extended to account for further gender inference methods.