Indicators that attempt to gauge wellbeing have been created and used at multiple spatial scales around the world. The most commonly used indicators are at the national level to enable international comparisons. When analyzing subjective life satisfaction (LS), an aspect of wellbeing, at multiple spatial scales in Australia, variables  that are significantly correlated to LS at smaller scales become less significant at larger sub-national scales. The reverse is seen for other variables, which become more significant at larger scales. Regression analysis over multiple scales on three groups (1) all individuals within the sample, (2) individuals with self-reported LS as dissatisfied (LS ≤ 5), and (3) individuals self-reporting LS as satisfied (LS > 5), show that variables critical for LS differ between subgroups of the sample as well as by spatial scale. Wellbeing measures need to be created at multiple scales appropriate to the purpose of the indicator. Concurrently, policies need to address the factors that are important to wellbeing at those respective scales, segments, and values of the population. Over the past few decades, dozens of wellbeing indicators have been developed as a means of tracking societal progress, comparing countries, or developing policies . These iMost indicators are created to measure average wellbeing at a national level, while a few focus on a single small community. There are advantages and disadvantages to using either scale. An indicator at the national scale allows for tracking of overall national progress and for cross-country comparisons. However, national level indicators aggregate a large number of individuals. This aggregation process may overlook critical information about portions of the population that have the dangerously low wellbeing and are most in need of community and government support . It alsoAt the opposite end of the spatial scale, small “boutique” community indicators have grown in popularity around the world . These iIn addition to scale, the variables used to calculate a wellbeing indicator define how that indicator can be used and what aspect of societal progress it measures. The structure and composition of wellbeing indicators has varied significantly, and has included (1) adjusted economic measures, that go beyond GDP to incorporate aspects outside of the market economy, (2) subjective measures collected through surveys, and (3) weighted composite measures .In this paper, we analyze the relationship between categories 2 and 3, from the list above. We use life satisfaction (LS), one component of wellbeing that looks at individuals’ satisfaction with their own lives, as the subjective measure. Here, we consider objective variables as predictors, or correlates, of subjective LSs. However, we acknowledge that objective variables can also be measures, or proxies, of wellbeing itself, independently of subjective LS. Subjective LS may vary depending on cultural norms or individual perceptions . Also, tOne example where perceptions and observations can become disconnected is around crime. Polices reducing crime levels may be effective, but the perception of crime may remain high. This may occur as a consequence of a personal experience of crime, lack of satisfaction with community cohesion, concern with the opportunities for the next generation, anxiety over the state of the economy, or simply overemphasis of crime in the media . NeverthHowever, the majority of wellbeing indicators focus on the use of objective variables. Objective variables are generally easier to measure and to directly target through policy interventions at societal scale. They can also target aspects that contribute to wellbeing but may not be perceived by individuals. For example, ecosystem services, such as water purification or carbon sequestration, are not directly perceived by individuals but are crucial to human wellbeing . InequalHow a wellbeing indicator is structured, and which variables are used, needs to reflect the values, goals, and cultural priorities of the individuals within that society. Once those variables are established, and their contribution to LS and wellbeing verified, aggregated measures can be used to monitor regional or national progress. These indicators can then influence people’s daily lives by shaping policy. Understanding how policies, local and sub-national, are perceived and at which spatial scales they are perceived, will improve understanding of the impact of those policies . FurtherAlthough extensive research has been published around scaling based on biophysical characteristics, little has focused around the relationship between wellbeing and spatial scale . As far We estimate the relationship between subjective wellbeing, as measured by self reported LS, and aggregated objective variables representing natural, social, human, and built capital, over different spatial scales.1This paper uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Social Services (DSS), and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views reported in this paper, however, are those of the author and should not be attributed to either DSS or the Melbourne Institute.. LS is taken from responses to the question, “All things considered, how satisfied are you with your life?” Responses are given on an 11-point Likert scale where 0 means totally dissatisfied and 10 stands for totally satisfied.For this study, we aggregate overall individual level LS scores derived from waves 1–16 (collected in 2001–2016) of the Household, Income and Labour Dynamics in Australia (HILDA) SurveyWe acknowledge that calculating the mean of Likert items can be problematic, especially not knowing whether increments in scale correspond to equal increments in the underlying latent variable. Treating LS as ordinal vs interpersonally cardinally comparable is a contentious issue in literature. Justifications for cardinality shows that treating LS data as cardinal yields similar results to treating it as ordinal, and both assumptions are compatible with LS scores . FurtherThe objective variables used in this study include eight related to human capital, four to social capital, and three to built capital . One natural capital proxy variable ) was included (discussed below) as no natural capital variables are collected as part of the HILDA Survey. Natural capital has been shown to have a significant impact on LS . HoweverThe objective variables were aggregated for individuals living within a given geographic area. We aggregated continuous variables by calculating the mean value per given area. For example, the mean household disposable income for a given area was calculated. Categorical variables were aggregated by obtaining the proportion of individuals of a specific category out of the total individuals within each respective area; for example, the proportion of men, the proportion of university graduates, and the proportion of those with a long-term health condition within each area.http://www.bom.gov.au/jsp/awap/ndvi/archive.jsp?colour=colour&map=ndviave&period=month&area=nat), derived from satellites.We use the NDVI as a proxy for natural capital. NDVI measures the amount of live green vegetation present. The source of the NDVI data is the Australian Government Bureau of Meteorology  was intersected with files containing the boundaries of the statistical geographic scales used in this paper. The average NDVI score per geographic region, weighted by spatial area, were calculated using the proportion of each region’s area represented by different NDVI scores. These scores per region were calculated at each scale in turn, providing the data for inclusion within the regression described above.2For example, when estimating the areal rainfall across a large geographic area the selection of the appropriate rain stations data to use, and how this should be aggregated, forms an entire body of literature of its own.. Although NDVI is also not directly perceived, it does provide a more direct proxy for natural capital as it shows location of all nature and its degradation  to neutral (5) and individuals who responded as having a LS score between just above neutral (6) and completely satisfied (10). For brevity, we refer to the full sample as “All,” sample 2 as “Satisfied,” and sample 3 as “Unsatisfied.” Using LS subsamples as a means of understanding different portions of the population has been previously used in literature . In thisBesides our current approach of separating “satisfied” and “unsatisfied” at a response of 5 and below and above 5, we also attempted to separate the total sample through other methods. In this respect, we attempted creating separation points using the median of LS and LS quartiles. Both of these separation points showed insufficient variation between the subsamples and the sample size were too small to make an informed analysis. Our two current subsamples provide a meaningful split in the population in determining what are the different contributions to the LS for each of the subsamples and why. Most importantly, it gives us additional information on the portion of the population that is most at risk.i,t is average LS of individuals in area i in year t, i,tX are the objective variables for area i in year t, λt is the year fixed effect, and i,tu is the area level idiosyncratic error.We examine the relationship between average LS and objective variables using the following empirical model:Our regression model uses pooled ordinary least squares controlling for time fixed effects and using cluster-robust standard errors. Our choice of estimation method is based on a number of factors. The Hausman test indicates the possibility of unobserved characteristics being correlated with the objective variables included in the model, for all samples, (at 1% significance level for SA1–SA4 and at 5% level for state) thus the use of random effects estimation would be inappropriate.We also ruled out extremely large time invariant area level fixed effects. Our unit of estimation is not the individual but an aggregate of individuals within a given statistical area. It is important to note that the composition of individuals being aggregated within a given area changes across the years. p < 0.01). Although the LS of individuals may not be stationary over the course of a lifetime, we do not use individual LS but aggregate LS in this paper. Aggregated data is prone to be more stationary as individuals within the aggregated data vary in age and may migrate between areas. Regardless, we acknowledge that unobserved area level fixed effects could be present. Nevertheless, our main interest lies in variation between areas rather than within areas over time. Most importantly, the aim of this paper requires us to examine how the preciseness of the estimates changes as we move up the spatial scale. We do not make strong causal or correlational inferences on the relationships.We conducted Fisher-type Augmented Dickey Fuller tests under various assumptions on each of the SA levels to ensure that aggregate LS within areas was stationary. The tests strongly reject the null hypothesis that all the panels contain unit roots  all individuals within the 16 waves that responded to the LS question, (2) individuals that replied that their LS was equal or less than five, and (3) individuals that replied that their LS was greater than five. The latter two samples enable us to examine how information relevant to individuals, or small groups, particularly for those at opposing ends of the LS Likert scale, is lost as we aggregate.For each of our samples, we aggregate individual level data to five spatial scales. The spatial scales used in this paper are based on the Australian Statistical Geography Standard (ASGS) hierarchical scales to allow for future comparison. Listed in ascending order, they include Statistical Area Level 1 (SA1), Statistical Area Level 2 (SA2), Statistical Area Level 3 (SA3), Statistical Area Level 4 (SA4), and State and Territory (State).http://www.abs.gov.au/websitedbs/D3310114.nsf/home/geography). Their sizes are based on population, not area, and each SA level aggregate up to the next SA level. SA1s have populations of approximately 400 (between 200 and 800) people and aim to separate differing geographical characteristics. SA2s have average populations of about 10,000  people and were designed to represent communities that interact economically and socially. SA3s have between 30,000 and 130,000 people and were designed to include an entire region that contains a small town or suburb. SA4s have between 100,000 and 300,000 people and were setup to reflect labor markets within regions. States and territories boundaries are defined by the federal government.The Australian Bureau of Statistics designed the SA geographic structure specifically for the release of statistical information . This also indicates that key information about dispersion of individual LS scores is lost as we aggregate across larger spatial scales . This is an important finding that is reflected in our regression results.This can be seen when the average LS is mapped across Australia at the five different spatial scales –1E. The Average LS for the “Unsatisfied” sample is around 4.2 and 8.1 for the “Satisfied” sample, at all spatial scales. The “Satisfied” sample has greater standard deviation in individual LS within areas compared to the “Unsatisfied” sample at all scales except state. In contrast, the “Unsatisfied” sample as greater standard deviation between areas compared to the “Satisfied” sample.Other variables seem to have major differences between the “Unsatisfied” and “Satisfied” samples. For example, at SA1, in the “Unsatisfied” sample the average portion of individuals that don’t exercise at all is about 0.2% and in the “Satisfied” sample, this is around 0.08%.These averages are similar at all scales. We also see that the proportion if individuals with a bachelor’s degree or higher qualification is great in the “Satisfied” sample as is the proportion of individual’s in a relationship, and the number of hours worked per week. The objective variables that seem to be smaller at all scales in the “Satisfied” sample include the proportion of individuals that are unemployed, divorced, engage in no physical activity at all (as discussed above), and having a long-term health condition.p ≤ 0.05) based on previous research, and at SA1 scales. The number of observations that are stated in The objective variables chosen for this regression were based on those that had a highly significant correlation to subjective LS at the individual level  (The model goodness of fit as measured by the adjusted  sample) . This in sample) . MoreoveAs the resolution of the scale decreases (aggregation area becomes larger), so does the uncertainty of our model as the estimated coefficients lose precision. This is seen in the higher robust standard errors relative to the coefficients. The increase in uncertainty is due to a larger number of people being aggregated in larger areas, overlooking individuals on the extremes. Further as evident from the standard deviations of the variables between areas , there ip = 0.225). As the scale gets larger, estimates for more variables lose their precision and become statistically insignificant.In the “All” regression, we find that most of the variables are highly significant at the SA1 level, except for the variable showing the proportion of individuals under the poverty line , household disposable income (positive) , educated to at least bachelor’s degree level (negative) , being in a relationship (positive) , and suffering a long-term health condition (negative) . Those variables that are significant at scales SA1–SA3 include: proportion of individuals that immigrated from an English-speaking country (positive), hours worked (negative), hours spent doing volunteering or charity work (positive), and having two or more children (negative), all with p < 0.001. However, there are some variables that do not become significant until the state level, and others that change signs at the state level. The environment (as measured by NDVI) is highly significant at the smaller scales of SA1 and SA2 (p < 0.001).The results also highlight a few variables that remain significant across the spatial scales. The variables that are significant at all scales except state include: proportion of individuals that were born in Australia (positive) (p < 0.001) as the spatial scale increases. The variation in individual LS within areas is higher at larger spatial scales. This has a significant effect in reducing the area level aggregated LS score.The coefficient for the average standard deviation of LS within an area increases in magnitude and remains statistically significant .p < 0.001) at each spatial scale, including SA1. There are also variables that, although remained statistically significant, changed signs from the full sample at SA1 level, including proportion of individuals that were born in Australia (now negative) (p = 0.019), immigrants from English speaking countries (now negative) (p = 0.032), and the number of hours worked per week (now positive) (p < 0.001).Compared to the full sample, fewer variables were highly significant (p < 0.001). Another important difference is that time fixed effects are not statistically significant for this sample.For this sample, weighted average NDVI and proportion of individuals below poverty line are not statistically significant at any scale. Mean household disposable income is only significant at SA1 . The proportion of individuals that identify as Indigenous is highly significant at SA1, SA2, and the state level (p < 0.001); however, it has a positive relationship at the first two scales, but a negative relationship at the state level. Another difference is that the proportion of individuals that do not engage in any physical activity only has a significant correlation at SA1 (p = 0.022).However, there are some important differences between regression “All” and regression “Satisfied.” NDVI is significant at all spatial scales, except at SA4 , are not significant at the higher scales of aggregation.Many variables that are significant to individuals or at small spatial scales become statistically insignificant as the aggregation area grows. Variables such as unemployment, engages in physical activity, divorced, having children, and many others are highly significant and have the largest marginal effects at SA1 level (p < 0.001), proportion of individuals that are below the poverty line (positive coefficient) (p < 0.005), NVDI (positive coefficient), (p = 0.028) and the standard deviation of LS (negative coefficient) (p < 0.001).On the other hand, other variables only become significant at the state level. When looking at the “All” sample, the only variables significant at the state level are proportion of individuals that speak English well (negative coefficient) (p < 0.001), but becomes insignificant in SA3 and SA4. Interestingly, it becomes significant again at the state scale, but with a negative coefficient. At smaller scales, immigrants have been shown to have a more difficult time integrating into their communities and accessing basic needs like health care  across all the scales is the standard deviation of LS. This is true in the samples “All” and “Unsatisfied.” This implies that as the inequality in LS increases amongst the population, the LS of individuals decreases. This is especially true amongst those that indicate that they have a low satisfaction with life (LS ≤ 5). In the case of the sample “Satisfied,” we found that only SA1 and SA2 were significant with positive coefficients. This shows that on a community level, those that have a high satisfaction with their lives (LS > 5), higher inequality increases their LS; that is, in the case of those that are already satisfied with their lives (LS > 5), seeing other individuals in their community with a lower LS serves to increase their own.Problems with aggregation are also seen when we map average LS across Australia at the different spatial scales . At the Any time data is downscaled or upscaled, as in this paper, assumptions are required about that data’s distribution. This inherently involves loss of data variation and an increase in uncertainty. The use of aggregated data that has greater uncertainty than certainty, may create spurious correlations, or correlation that cause incorrect inferences . In thisOn the other hand, using large-scale data without aggregation creates big data problems. Looking at LS data of every individual in Australia is logistically unrealistic and provides a lot of “noise,” which makes it impossible to inform policies at reasonable scales.Aggregating LS scores, and estimating the models separately, for satisfied and unsatisfied individuals shows how the relationship between objective variables and LS differs between the two groups . SeparatIn our model, we used the NDVI as an indicator of natural capital, which has been shown to be a good proxy for ecosystem services and nature’s contributions to humans .3Selecting the most appropriate proxy for nature (from a very wide range of possibilities) is a non-trivial task, which we leave for future research. and is often not perceived to have a direct impact on LS. Previous studies have shown that proximity to nature has a direct impact on both physical and mental health . At the SA2 scale, environment has the third largest marginal value of all the variables. This shows that those satisfied with their lives, nature provides a positive contribution to their wellbeing at both small community scales and at the state level. The individuals have the time and the disposable income, and the health to enjoy nature.However, regressions “Unsatisfied” and “Satisfied” show that individuals with low LS (LS ≤ 5) perceive the environment differently than those with a high LS (LS >5). Those individuals with a high LS, we find NDVI significant at most scales (except at SA3 and SA4 levels) with a positive coefficient . This implies that at larger scales, with aggregation, household income is not a major contributor to the reported low level of LS. Other factors have a great impact on LS, such as long-term health conditions. However, for individuals with a higher LS, household income is positively and highly significant at all scales besides state. For these individuals, additional household income does have a positive impact, which means that they may have more time and opportunities to use this additional household income.Another major difference between regressions “Unsatisfied” (LS ≤ 5) and “Satisfied” (LS > 5) is in the variables of household income and poverty. For individuals with a lower LS, household income is significant and positive at only SA1  and state/territory scales (p = 0.013) in the “Satisfied” sample. Research has shown that LS comes not from absolute income but relative income  and not the “Unsatisfied” regression at any scale. This implies that individuals that are already satisfied with their lives, and have the time and resources to volunteer their time, receive a positive contribution from volunteering to their LS. Those that are not satisfied with their lives, find that volunteering neither provides a positive or negative contribution to their LS, probably because they have their own worries and do not have the time or other resources to enable them to volunteer.Volunteering is a proxy that has been used within previous research to represent social capital . We findp = 0.006 and p = 0.017, respectively), it is most likely capturing another demographic aspect.There are stark differences in the regression results for the “proportion of individuals that speak English well,” both across spatial scales and between samples . It is wObjective variables that are significant at the SA1 level often cease to be significant at the higher levels, especially at the state level. By focusing on only a single scale, information about objective factors that contribute toward LS may be lost through aggregation. For example, decisions regarding environmental policy based on state level indicators may fail to reflect the important contribution that the local environment, like community parks, makes to individual wellbeing at the local area level. As seen in regression 2 and 3 in When creating national or subnational wellbeing indicators, it is critical to understand the differences in the value sets that different portions of the population hold, arguing for focusing on finer and more appropriate scales. These value sets can vary due to a person’s age, where they grew up, whether they immigrated, and many other factors. For example, in Australia, the Indigenous population comprises 3.3% of the total population, just fewer than 800,000 individuals according to the 2016 census. However, the values they hold and the type and relative importance of factors that contribute to their LS has been found to differs greatly from others in Australia . Even inMany wellbeing indicators are created based on information only from the sub-national or national scales. This creates a situation in which society is focusing on variables that may not be significant to individuals, and omitting those factors crucial at local level, such as the environment. Often, policies are set based on the assumption of improving variables contributing to human wellbeing. However, the scale of these policies is not often aligned with the most appropriate scale of the variable, potentially reducing the effectiveness of the policy intervention.By only considering very fine scales of wellbeing, critical information around overall wellbeing of a nation may also be overlooked. Assessing wellbeing at a national scale illustrates the overall progress of the entire nation. A series of national level goals can provide a dashboard of wellbeing indicators; however, an aggregated overall indicator is also essential in measuring the wellbeing of the entire population . A natioAlthough a range of different approaches to measuring a population’s wellbeing have been proposed over the last few decades, consensus has proved difficult to achieve. How a person perceives his or her wellbeing is difficult to assess, directly or indirectly. Nevertheless, measuring progress within society through improved individuals’ wellbeing requires a quantitative evaluation.However, creating measures that assess every individual globally is impractical. Aggregated measures are commonly used to assess a population’s wellbeing. The process of aggregation, however, changes how we perceive and measure wellbeing, potentially making the measures inappropriate or meaningless. For example, does the average wellbeing of people within a region have a relationship with that regions average income? Or the average age of that population? It also changes the relationship between objective and subjective variables, where objective variables are meaningful at one scale and become meaningless at another. It is critical to understand how measuring wellbeing at different scales affects the measures themselves and what it means for the population.This research has posed the question of what is the ideal scale for a wellbeing indicator? Unfortunately, there is no easy or simple answer to this question. It is critical to analyze the small scales to ensure that the individuals and communities that are most at risk are identified and assisted. Pinpointing the limiting variables of those communities provides critical information to addressing the dearth in self reported LS. This will also ensure that the inequality in LS diminishes within a region or country. On the other hand, sub-national and national indicators are crucial in assessing the progress and wellbeing of a nation as a whole.Before a global consensus around a preferred indicator, or dashboard of indicators, is achieved, it is critical that the purpose of this indicator is defined. An indicator used for national comparisons will be vastly different to an indicator that tries to identify individuals most at risk within a population. Both of these indicators require different policy interventions and have varying impacts. For both types of indicators, a better understanding of the relationship between objective and subjective indicators , the impact of aggregating at different special scales, aggregation methods, and data availability is required.10.7717/peerj.6502/supp-1Supplemental Information 1Click here for additional data file.


How can the history of research ethics be expanded beyond the standard narrative of codification—a story that does not reach back beyond World War II—without becoming so broad as to lose all distinctiveness? This article proposes a history of research ethics focused on the “scientific self,” that is, the role-specific identity of scientists as typically described in terms of skills, competencies, qualities, or dispositions. Drawing on three agenda-setting texts from nineteenth-century history, biology, and sociology, the article argues that the “revolutions” these books sought to unleash were, among other things, revolts against inherited conceptions of scientific selfhood. They tried to redefine the scientific self in their respective fields of inquiry by advocating particular catalogs of virtues or character traits. These ideals of selfhood, their contested nature notwithstanding, translated into practice in so far as they influenced hiring and selection policies and found their way into educational systems. The project of reclaiming the scientific self as an important subject of study in the history of research ethics is not an antiquarian pursuit, but related to an ethical question faced by scientists today: How are their scientific selves being shaped by funding schemes, research evaluation protocols, and academic hiring policies? The history of research ethics has often been reduced to the history of its codification. It has been reduced, more specifically, to codification in scientific codes of conduct such as the Nuremberg Code (1947) and the Declaration of Helsinki (1964). Textbooks on research ethics testify to the enduring power of this view when they trace the history of their field back to codification issued in response to medical experiments in Nazi Germany  and “research ethics”  Baker : 9–10 bySo how can the history of research ethics be expanded beyond codification (the standard narrative) without losing all distinctiveness (the risk implied in the revisionist approach)? This article offers a way out of the dilemma by proposing a history of research ethics focused on the “scientific self,” that is, the role-specific identity of scientists as typically described in terms of skills, competences, qualities, or dispositions —this article argues that the “scientific revolutions” these books sought to unleash were, among other things, revolts against inherited conceptions of scientific selfhood. All three authors tried to redefine the scientific self in their respective fields of inquiry by advocating particular catalogs of virtues or character traits. This article subsequently shows that these ideals of selfhood, their contested nature notwithstanding, translated into practice in so far as they influenced hiring and selection policies and found their way into educational systems. By way of conclusion, it is argued that the project of reclaiming the scientific self as an important subject of study in the history of research ethics is not an antiquarian pursuit, but related to an ethical question faced by scientists today: How are our selves being shaped by funding schemes, research evaluation protocols, and academic hiring policies?Drawing on three agenda-setting texts from nineteenth-century history, biology, and sociology—Leopold Ranke’s  Species , and ÉmiOrigin of Species, to start with, is usually read as “one long argument” about natural selection as a primary mechanism for biological evolution. Yet at the same time, Darwin’s book is a treatise about the study of nature and, more specifically, the character traits that its author regarded as indispensable for a scientific naturalist. Judging by the opening paragraph, in which Darwin described the years of study that had gone into the book, patience and perseverance were prominent among these character traits: “I hope that I may be excused for entering on these personal details, as I give them to show that I have not been hasty in coming to a decision” , emphasized Darwin’s “extraordinarily active imagination” in developing bold hypotheses  dispositions or character traits. But how could this be done? How could virtues be acquired and vices be unlearned, especially if this challenged established views on the nature of a good scientist?Ranke, Darwin, and Durkheim all dwelt at length on the efforts this required, meanwhile offering psychological explanations as to why their intended revolutions were long overdue and unlikely to win many hearts. Durkheim most explicitly pointed to the power of unconsciously acquired mental habits when he argued that no scientist is likely to overcome all prejudice: “The mind has such a natural disposition to fail to recognise” the power of preconceptions “that inevitably we will relapse into past errors” Durkheim /1895: 72Origin of Species (“nor do we know how ignorant we are”)  Darwin . SimilarDurkheim /1895: 38Durkheim : 642 to historische Übungen—an informal seminar for advanced students that soon acquired the symbolic significance of a rite of initiation into the historians’ guild  that socialized emerging sociologists into an ethos of objectivity by making them cooperate with each other, under Durkheim’s supervision. Darwin’s followers, too, agreed on the importance of “mental discipline,” although Huxley added that “enthusiasm for truth” is usually best kindled by exemplary figures who show in concrete detail what a scientific self looks like  Assuming that virtue language largely fell into disuse in the twentieth century, how do the virtues advocated by Ranke, Darwin, and Durkheim relate to the moral language prevalent in twentieth-century codes of conduct? Specifically, is there sufficient continuity to allow for long-term comparison? (2) Virtue is a relevant category as long as individuals can put their stamp on scientific research. But to what extent was that the case in the Cold War military-industrial-scientific complex, or is that the case in today’s globally entangled world of science? Does the scientific self still matter in an age of “science 3.0” Miedema ?As for the first question, the assumption that virtue language has disappeared in the course of the twentieth century is in need of empirical testing. Whereas the generic term “virtue” has come to be imbued with Victorian connotations, this is not typically the case for dispositions conventionally classified as virtues, such as accuracy, precision, and objectivity. Whether scientists in, say, 1970 spoke less often about accuracy, precision, or objectivity than their predecessors in 1870 is a question still awaiting detailed historical inquiry. Additionally, it would be erroneous to think that codes of conduct had no space for virtue language. Codes of conduct were, and are, a heterogeneous genre, especially in that they often drew eclectically on several moral languages at once. Although it seems that early codes, such as those issued by the American Association for the Advancement of Science  and the Secondly, even if detailed follow-up research would demonstrate an overall decline in virtue language, this would not imply that scientific selfhood became obsolete in an age of “science 3.0.” As Steven Shapin forcefully argues, the increase in scale and complexity characteristic of twentieth-century scientific research in and outside of academia did not produce an “elimination of the personal” of a sort proclaimed by conservative and progressive social theorists alike. To the contrary, it made increasing demands on individuals’ personal abilities to navigate complex professional environments. Although twentieth-century research managers might have smiled at the virtues advocated by Ranke, Darwin, or Durkheim, their job advertisements reveal how much they continued to care about “imagination, initiative, resourcefulness, energy, persistence, judgment, honesty, accuracy, dependability, loyalty and cooperativeness”—all of which are personal dispositions Shapin : 184. Lihistorische Übungen to formalized codes of conduct. Yet what all these genres, discourses, and practices had in common is that they defined the scientific self and specified the expectations that scientists had to meet in order to be recognized as professionals. This, then, offers a suitable prism for a history of research ethics able to cover various periods, regions, scientific disciplines, and linguistic conventions. The history of research ethics proposed in this article is a history of demands placed upon the scientific self, in multiple languages, in several scientific genres, and in various research and educational practices.A history of research ethics focused on the scientific self is therefore not a history of virtues alone, but a history of multiple discourses and practices on which scientists in various times and places drew in articulating, advocating, and implementing their research ethical standards. These discourses, in turn, were not confined to particular genres: they were used in different contexts, varying from informal expands the standard narrative, according to which research ethics emerged in response to World War II atrocities, into a longer and more intricate story about the demands that changing moral economies place upon scientists. Also, it broadens the subject matter of the history of research ethics by expanding its subject matter beyond ethical protocols. By putting the scientific self at center stage, it locates ethical reflection and formation right in the heart of scientific activity. Strategically, this encourages cooperation between historians of science and historians of research ethics. It invites the former to take seriously the ethical aspects of the scientific revolutions associated with the names of Ranke, Darwin, and Durkheim, just as it invites the latter to recognize that virtues such as advocated by these scientists were markers of a research ethics avant la lettre.This implies, by way of conclusion, that a history of research ethics focusing on the scientific self The Academic Self: An Owner’s Manual (Finally, by way of coda, a study of moral demands made on scientists in times and places other than ours is not an antiquarian project. Examining how scientific selves were shaped and disciplined in earlier periods of history implicitly raises the question how scientific selfhood is molded in our days. What kind of ethical standards are implied in today’s academic reward structures? What sort of scientific conduct do competitive research funding schemes encourage (Mountz et al. s Manual ? Histori


The introduction of the two-child family planning policy in China calls for a study of the response of mothers’ subjective well-being after the birth of a second child. Generally focusing on Western countries, previous studies suggested that a series of factors could influence the response, but insufficient attention has been paid to the relative importance of these factors so far. Based on survey data from mothers of two children in the Xi’an metropolitan area, Shaanxi Province, China, our study indicates that the important factors associated with mothers’ life satisfaction after having a second child were, in general, common to Western countries and China. There were also two factors somewhat unique to China: positive adjustment  by firstborn children  following a sibling’s birth, predicted enhanced life satisfaction for mothers; additionally, mothers who had both a son and a daughter reported the highest increase in life satisfaction, while mothers who had two sons reported the lowest increase. Socioenvironmental constraints  had a larger association with mothers’ life satisfaction than individual ideational factors . These findings suggest that fertility-friendly policies and convenient family intervention institutions are needed to alleviate potential undesirable consequences and improve maternal life quality following a second childbirth so that the two-child policy can be a success. The introduction of the two-child family planning policy since the end of 2013, initially selectively and then universally, has marked a major social transition in China; for decades before that time, almost all urban couples and some rural couples were not allowed to have a second child after having their first child in the country  . Thus, From the early 1980s to the early 2010s, the one-child policy was implemented in China. According to the policy, almost all couples in urban areas were allowed to have only one child ; in ruraIn October 2015, the government of Xi’an, a metropolitan city in Western China, requested a research team, with the first author as the principal investigator, to investigate the situation of implementing the policy and whether one of its major goals was fulfilled: happiness of couples with one child improved after they had their second child ; actuallAll subjects gave their informed consent for inclusion before participating in the survey. The survey was conducted in accordance with the Declaration of Helsinki and was approved by the Biomedical Ethics Committee of Xi’an Jiaotong University (No. 2015-636).Since the first cohort of second-born children under the selective two-child policy were born around September 2014, we sampled all women in only-child couples  who gave birth to their second child within one year from that time, i.e., September 2014 to August 2015. They were distributed across all 15 districts/counties (the first level of administrative division) of the Xi’an metropolitan area. The effective response rate was relatively low: most of them could not be contacted or failed to contribute to an effective questionnaire, due to issues with feeding, childcare, conflict with work time or relocation. Finally, 596 effective questionnaires were successfully collected through either a telephone or self-administered approach. Studies based on data across the whole country indicated that only-child couples and other couples did not differ significantly in desire and intention to have a second child, and life satisfaction following a second childbirth ,36. ThusIn the questionnaire, the key question asked was: “Compared to the time before having the second child, how do you think your current life is?” Available options were: “much less satisfied”, “somewhat less satisfied”, “the same satisfied as before”, “somewhat more satisfied” and “much more satisfied”. Our indicator of subjective well-being after a second childbirth was borrowed from a measure of well-being in the British Household Panel Survey  and a mBesides the key question, the following six groups of questions were asked: (1) Background information: family’s annual income, wife’s and husband’s occupation and education, wife’s age, time since the second birth, the firstborn child’s age at time of survey, and family settlement (main city vs. inner suburb vs. outer suburb). (2) Postnatal depression in terms of whether the mother felt depressed during the first month after the second childbirth . (3) Fertility preference: fertility desire before the second birth ; the occurrence of an unintended pregnancy ; and the sex composition of two children. (4) Responses of the husband and the firstborn child to having a second child . (5) Factors constraining parenting . (6) Family orientation in terms of whether the mother was willing to sacrifice career development for parenting two children. R [j took values of 1, 2, ···, J − 1  and Y up to j. In this study, J = 3; thus, the base model actually consisted of two logistic regression models, each with a j-specific intercept jθ and a set of iβ . Since there was a minus sign after jθ, a positive iβ implied a lower likelihood of having decreased life satisfaction after the birth of the second child. A model check indicated that the proportional odds assumption was violated for some predictors, i.e., iβ varied with j, so we adapted the above model accordingly to get a partial proportional odds model . For example, a regressor might be related to a lower likelihood of feeling less satisfied after having a second child, but not necessarily to a higher likelihood of feeling more satisfied—i.e., 1 − likelihood of experiencing reduced or invariant satisfaction—or vice versa. The following proverb helps to understand the phenomenon: “Money cannot buy happiness, but buy off unhappiness.”.In the statistical model analyzing the influence of factors mentioned above on mothers’ subjective well-being, the response variable was the mother’s life satisfaction after the second childbirth, an ordinal variable with three levels, coded as 1: “less satisfied”; 2: “the same satisfied as before”; and 3: “more satisfied” , we colR ,39,40,41c = AICc,simplified − AICc,base) was used to assess the relative importance of the predictors. If ΔAICc was negative, the excluded predictor was not important, as excluding it from the base model led to a smaller distance between the model and reality; by contrast, if ΔAICc was positive, the predictor was considered as important. The larger the ΔAICc value, the more important the predictor in predicting the response variable.The relative importance of the given predictors was assessed with a likelihood-based method ,42,43: . Three-quarters of the surveyed mothers or their husbands had finished higher/tertiary education. Most second births resulted from family planning, 7.9% from mistimed pregnancy, and 19.3% from unwanted pregnancy. Note that the study only investigated mothers whose pregnancy resulted in a live second birth, and the unintended pregnancy rate could be higher if all pregnancy outcomes are considered, including live births, abortions, stillbirths, etc. About 20% of mothers felt depressed within the first month after the second birth. During the pregnancy, a minor proportion of husbands and firstborn children did not show an explicitly supportive attitude about having a second child. Close to 57% of firstborns became happier after seeing their sibling. For about 30% of mothers, the husband improved his share of housework after the birth of the second child, but for others, such an improvement did not happen. Three-quarters of mothers felt moderate or severe conflict between work and parenting two children, and about 71% felt moderate or strong pressure in parenting two children. Finally, 22% of mothers were very willing to sacrifice their career development to parent two children, 35% were only moderately willing to do so, and 18% were reluctant to do so; other mothers said they were not professional women and thus this question was not applicable to them.The factors that significantly influenced post-second birth life satisfaction were as follows: (1) Background factors. Older age predicted a higher likelihood of decreased life satisfaction (odds ratio (OR) = 1.09, 95% confidence interval (CI) = 1.00, 1.19; All three indicators of fertility preference were significantly related to life satisfaction after the second birth. First, a weaker desire to have a second child before the birth was negatively associated with the likelihood of enhanced life satisfaction after the birth . Second, mothers who had a second child due to an unintended pregnancy had more difficulty feeling increased life satisfaction than those whose second child was a result of family planning . Third, compared to those with two sons, mothers with other gender combinations of children were more likely to feel more satisfied after the second birth: first child a son and second a daughter, OR = 2.22 ; first child a daughter and second a son, OR = 1.72 ; both daughters, OR = 1.53 .p = 0.38). The husband’s emotional support during the pregnancy was positively associated with the likelihood of enhanced life satisfaction for the mother after the birth . Compared to the case where the husband increased his share of housework after the second birth, there was a lower likelihood of feeling more satisfied if the husband’s share did not increase .Responses of family members to the second child constituted another group of significant influencing factors. Compared to the case where the firstborn child became happier after the birth of a sibling, the case where the level of happiness was the same predicted a lower likelihood of enhanced life satisfaction , and the case where the firstborn child became less happy predicted a higher likelihood of decreased life satisfaction for the mother . The firstborn’s emotional support for the mother during the pregnancy with the second child was not significantly associated with the likelihood of enhanced life satisfaction of the mother after the birth . It is wBoth constraining factors had a major effect on life satisfaction after the second birth. Compared to the case where having a second child did not have a negative influence on career development, a moderately negative effect would bring a lower likelihood of enhanced life satisfaction after the birth . If having a second child had a strong negative influence on career development, the likelihood of feeling less satisfied with life would be enhanced , and the likelihood of feeling more satisfied declined after the second birth . Compared to the case of feeling no pressure in parenting two children, feeling strong pressure meant a higher likelihood of decreased life satisfaction , but such pressure did not significantly predict the likelihood of enhanced life satisfaction ; evidently, strong pressure was related to a lower likelihood of feeling the same satisfaction as before having the second child.Lastly, compared to mothers who were very willing to sacrifice their career development to parent two children, other mothers would have a higher likelihood of feeling less satisfied and a lower likelihood of feeling more satisfied after the second birth .c (36.09), indicating that these were the most important factors related to the mother’s life satisfaction. Other important groups of modifying factors included fertility preference , the mother’s family orientation , feeling depressed during the first month after the second childbirth (ΔAICc = 6.16), and the firstborn’s happiness (ΔAICc = 3.51). This sequence of ΔAICc corresponds to the relative importance of these groups of factors in modifying the mother’s life satisfaction after a second birth.Likelihood analysis indicated that removing two socioenvironmental constraints on parenting children—perceived parenting pressure and work–family conflict—caused the largest increase in AICBased on survey data from Xi’an City, China, we conduct a relatively comprehensive investigation of the impact of individual, familial and social factors on mothers’ subjective well-being after a second birth, which is timely because the two-child policy has been in effect fairly recently. Our study indicates that the well-studied happiness modifiers in Western countries—e.g., parenting pressure, work–family conflict, family orientation, and partner’s emotional and practical supports—were also significant for Chinese mothers after having a second child.Furthermore, the current study provides one of the first evaluations of the relative importance of these factors: post-second birth life satisfaction was more subjected to socioenvironmental constraining factors than to individual ideational factors, a finding consistent with previous studies on affective vs. cognitive subjective well-being ,44. ThroThis study also obtains important results regarding the influence of family members. First, consistent with our expectation, the firstborn child had an important influence on the mother’s life satisfaction after the second birth. Kohler and Mencarini  previousSome factors did not show an expected influence on mothers’ subjective well-being; e.g., sex composition of children had an influence contrary to the expectation based on assumed son preference and firstborn’s emotional support during the pregnancy was not significant. Presumably, so-called son preference might have waned in the survey area and now is not as strong as we generally think; instead, both the current study and recent fertility surveys suggest that a mixed-sex preference may have truly become prevalent in China, just as in Western countries . This study has important implications for China and possibly also for other low-fertility countries. First, a substantial fraction of mothers, close to 20%, experienced a decrease in subjective well-being after having a second child. The figure for mothers in general might be similar . This fDespite the contributions discussed above, limitations of the study should be mentioned. First of all, a retrospective research design made the measurement of some indicators suffer from potential reporting bias. For example, a mother who experienced decreased life satisfaction after having a second child more likely reported postnatal depression, work–family conflict, etc. A prospective study based on a longitudinal survey could help to fill the gap. Since the selective two-child policy was introduced for only one year at the time of the survey, it was difficult to collect follow-up data; however, a longitudinal study should and can be done in the near future to validate or invalidate our findings.Additionally, there were a few potential sampling biases. First, the low effective response rate (~24%) implies that we might have missed mothers who were busy with their work or other responsibilities. Second, the proportion of mothers with a tertiary degree could have been over reported , which mLast but not least, we did not study the association of fertility and happiness in husbands, who may deal with different factors that reinforce or challenge their post-second birth well-being. Given that a goal of the two-child policy is to improve couples’ well-being after having a second child, it is warranted to fill the gap in the future.The factors significantly influencing mothers’ subjective well-being were, in general, common to Western countries and China, such as parenting pressure, family orientation, work–family conflict, and housework division. However, two factors seemed to be unique to Chinese mothers: (1) due to restriction of the previous one-child policy, firstborn children were generally old enough at the time of the second childbirth to understand what was going on, and if they felt happier after a sibling’s birth, the mother tended to feel more satisfied with life; and (2) mothers with two sons had the lowest gain in life satisfaction, while those with a son and then a daughter had the highest gain. Socioenvironmental constraints had a larger effect on mothers’ life satisfaction than individual ideational factors. These findings suggest that fertility-friendly policies and family intervention institutions should be introduced to alleviate potential undesirable consequences following a second childbirth, e.g., the psychological pressure of parenting two children, firstborn’s negative adjustment, unwanted pregnancy, and postnatal depression . Such measures will help to ensure that maternal life quality tends to be improved after the birth of a second child and the two-child family planning policy is a success.


Our private connections can be exposed by link prediction algorithms. To date, this threat has only been addressed from the perspective of a central authority, completely neglecting the possibility that members of the social network can themselves mitigate such threats. We fill this gap by studying how an individual can rewire her own network neighborhood to hide her sensitive relationships. We prove that the optimization problem faced by such an individual is NP-complete, meaning that any attempt to identify an optimal way to hide one’s relationships is futile. Based on this, we shift our attention towards developing effective, albeit not optimal, heuristics that are readily-applicable by users of existing social media platforms to conceal any connections they deem sensitive. Our empirical evaluation reveals that it is more beneficial to focus on “unfriending” carefully-chosen individuals rather than befriending new ones. In fact, by avoiding communication with just 5 individuals, it is possible for one to hide some of her relationships in a massive, real-life telecommunication network, consisting of 829,725 phone calls between 248,763 individuals. Our analysis also shows that link prediction algorithms are more susceptible to manipulation in smaller and denser networks. Evaluating the error vs. attack tolerance of link prediction algorithms reveals that rewiring connections randomly may end up exposing one’s sensitive relationships, highlighting the importance of the strategic aspect. In an age where personal relationships continue to leave digital traces, our results empower the general public to proactively protect their private relationships. This raises privacy and security related concerns as our data may be valuable not only to enterprises and public entities, but also to cyber criminals who are increasingly relying on network analysis tools for malicious purposes4.As social network analysis tools become more sophisticated and widespreadlink prediction5. Intuitively, such tools analyse the topology of a given network in order to predict the connections that are most likely to form in the future5. These tools can also be used to analyse the observed network topology to identify connections that are hidden from the observer, either due to data scarcity, or due to the deliberate concealment of information6. Link prediction has numerous applications, ranging from providing recommendations in e-commerce7, through discovering the interactions between proteins in biological networks8, to finding hidden connections between terrorists9 or criminals10. A plethora of link prediction algorithms have been proposed in the literature12. In this article, we focus on the mainstream class of such algorithms, which is based on similarity indices13, whereby the topology of a given network is analysed to quantify the similarity between any two disconnected nodes. Here, the underlying assumption is that the greater the similarity between two nodes, the greater the likelihood of having a link between them. These algorithms are extensively studied in the literature due to their scalability, effectiveness, and robustness13.Among the most fundamental network analysis tools are those designed for tell me who your friends are and I’ll tell you who you are”, a network analyzer may use link prediction algorithms to perform a link re-identification attack14, which not only reveals your undisclosed friends, but may also enhance the severity of the more general attribute inference attack15 whereby the goal is to infer various private information about “who you are”17. Worse still, even when link prediction is applied without malicious intentions, it can still have undesirable consequences. Consider the hundreds of millions of Facebook users across the globe who see link prediction in action every time they receive a “friend suggestion”. These suggestions become problematic when the “people you may know” include individuals you do not want to be associated with, e.g., due to their radical views or extreme ideologies, thereby making you guilty by association.If used with malicious intent, link prediction algorithms may constitute a threat to both the privacy and the security of the general public. In particular, inspired by the proverb “22 considered the problem faced by a data trustee who publishes or discloses anonymized network data to a third party for business, scientific, or statistical purposes. A straightforward anonymization strategy would be to remove any identifying attributes, and introduce some synthetic identifiers instead. However, such an approach is not immune to attacks, especially in scenarios where an attacker possesses some additional information or background knowledge which, if coupled with the published data, can be used to de-anonymize the network24. A more effective alternative is to introduce network perturbations by removing existing links and adding false ones. Broadly speaking, two main approaches have been proposed for this purpose25. In the first one, the perturbations are introduced randomly30. In the second approach, the perturbations are constrained so that the anonymization satisfies certain criteria such as, e.g., k-degree anonymity, which is satisfied when the degree of every node is the same as the degree of at least k − 1 other nodes32. Nevertheless, in both approaches, too many modifications may cause the network to lose its fundamental properties and even become worthless to legitimate users. Hence, this literature focuses primarily on how the data trustee should modify the network such that its desired properties are preserved and a satisfactory level of privacy protection is ascertained36. An implicit assumption in this literature is that the responsibility of protecting link privacy lies solely on the shoulders of the data trustee, implying that the members of the network are incapable of protecting their undisclosed connections by themselves.The issue of protecting link privacy has already been studied in the literature from various angles. More specifically, the network anonymization and de-anonymization literatureIn contrast to the aforementioned literature, in this article we do not consider the issue of protecting link privacy from the perspective of the data trustee but rather from the perspective of a network member. As such, our main goal is to understand how a self-interested individual should act in order to conceal her sensitive relationships. Unlike the data trustee, such a self-interested individual is not the least concerned with anonymizing the entire network nor preserving its properties. Instead, her sole objective is to protect her own privacy, regardless of the consequences on the network as a whole.seeker” runs link prediction algorithms, and a self-interested member of the network, called the “evader”, strategically rewires the connections within her neighbourhood in order to hide some of her relationships from the seeker. We study the optimization problem faced by the evader, and prove that it is extremely challenging for her to optimally hide those relationships from some of the most widely used link prediction algorithms. Based on this finding, we shift our attention towards identifying effective, albeit not optimal, solutions. In particular, we propose two heuristics that can readily be applied by users of existing social media platforms, without requiring any knowledge about the topology beyond their network vicinity. The first heuristic removes strategically-chosen links from the network, while the other adds new ones. We show that both heuristics are effective in practice, although the former is superior to the latter, suggesting that in order to hide a relationship, “unfriending” carefully-chosen individuals can provide a more effective disguise than befriending new ones. Furthermore, we evaluate the attack tolerance of different link prediction algorithms, and find that their resilience tends to increase with the number of nodes, and decrease with the average degree in the network. Finally, we compare the error vs. attack tolerance of link prediction algorithms, to evaluate the performance gain achieved when the evader acts strategically as opposed to randomly. We find that strategic modifications often bring significant gains, while random modifications can backfire, leaving the evader more exposed.In more detail, we consider a setting in which a “In an age where social media platforms are ingrained in our day-to-day lives, our privacy is becoming ever more vulnerable to invasion, and our ability to take a stand against such intrusion is now more pressing than ever. Keeping certain relationships hidden from everyone else is not as straightforward as it may seem. Indeed, with the widespread use of link prediction algorithms, it is no longer sufficient to simply avoid declaring those relationships. To make matters worse, such algorithms may arrive at erroneous conclusions, associating us with people we may not even know, and potentially tainting our reputation. We address these issues by proposing effective and practical heuristics that can be applied by users of existing social media platforms, thereby demonstrating how to hide one’s relationships from link prediction algorithms.G = , where V is the set of nodes and E is the set of edges, we will use the term “non-edge” to refer to any pair of nodes that is not in E, and will denote the set of all non-edges by evading link prediction involves a seeker who ranks all non-edges based on a similarity index 37 and the average precision (AP)38 , is the set of edges that can be added, andis the set of edges that can be removed. The goal is then to identify two sets, and, such that the resulting set, , is in:(Evading Link Prediction). b as well as the sets optimal way to spend this budget in order to hide a given set of non-edges:In this definition, we introduced the budget The problem of Evading Link Prediction is NP-complete for each of the following similarity indices: Common Neighbours39, Salton40, Jaccard41, Sørensen42, Hub Promoted43, Hub Depressed43, Leicht-Holme-Newman44, Adamic-Adar45and Resource Allocation46.. CTR, focuses on removing edges whereas the second, called OTC, focuses on adding edges.A discussion of the choice of similarity indices can be found in Section Closed-Triad-Removal) can serve an evader w wishing to hide connections in H by removing an edge,  ∈ E, such that:Our first heuristic, called CTR  from the network, the algorithm removes the closed triad whose nodes are v, w and x; hence the name Closed-Triad-Removal (CTR); see the pseudo-code in Section v, w and x form a closed triad, this is initially unknown to the seeker since  is undeclared, i.e., it is a non-edge as far as the seeker is concerned. Importantly, the removal of  can only decrease the similarity score of  according to any of the similarity indices outlined in Theorem 1; see the analysis in Materials and Methods. The algorithm can be even more effective if the removal of  results in the removal of multiple closed triads, each containing a non-edge in H. In Fig. v, w) decreases the similarity scores of not one, but three non-edges in H, namely ,  and . Based on this observation, the CTR heuristic is designed to maximize the number of such non-edges, by examining all possible choices of  and selecting one that affects the greatest number of edges in H.By removing  works by adding edges to the network, unlike CTR which worked by removing edges. Generally speaking, the goal of OTC is to “hide” a non-edge, e ∈ H, by decreasing the similarity score of e while at the same time increasing the similarity scores of (some of) the non-edges that fall within the neighbourhood of e. This, in turn, decreases the position of e in the similarity-based ranking of all non-edges, thereby reducing the likelihood of e being highlighted by a seeker armed with a link prediction algorithm. To achieve this goal, OTC rewires the network as illustrated in Fig. v, w) to be added to the network such that:Our second heuristic, called OTC  creates open triads–one containing x, v, w and another containing v, w, y—hence the name Open-Triad-Creation (OTC). Importantly, given the similarity indices outlined in Theorem 1, the addition of  in Fig. w, u) and can only increase that of  and ; see Materials and Methods for a more formal analysis. More generally, since the creation of an open triad can only increase the similarity score of the non-edge therein, the more open triads we create by adding  the better, since this may increase the similarity scores of a greater number of non-edges, all of which contribute towards reducing the position of  in the similarity-based ranking of all non-edges. Based on this observation, OTC examines all possible choices of , and selects one that results in the greatest reduction in the ranking of the non-edges in H, while ensuring that no other non-edge in H becomes more exposed during this process; see the pseudo-code in Section As shown in Fig. u and w wish to hide their relationship, then any one of them, say w, can send friendship requests to individuals whose list of friends contains as many people as possible who are not connected to w. Even if such individuals are hard to find, one can still send random friendship requests to highly-connected strangers, hoping that some of them would accept the request. This is indeed plausible, as an estimated 55% of people accept friendship requests from complete strangers on Facebook47. Nevertheless, when evaluating OTC empirically in the next section, it will only be permitted to add edges between the evader and the neighbours of her neighbours, since they are even more likely to accept friendship requests than complete strangers.OTC can be applied on popular social media platforms in a straightforward manner. For instance, if T, and a probe set, Q. The index trains on T and assigns a similarity score to every pair of nodes accordingly. These scores are then evaluated based on the area under the ROC curve (denoted by AUC)37, which can be interpreted as the probability that the index assigns a greater score to a random link in Q than to a random non-edge. With this in mind, we evaluate the effectiveness of each heuristic against a similarity index in a given network as follows: we run the heuristic iteratively, where each iteration involves either removing an edge (when running CTR) or adding an edge (when we run OCT). After each such iteration, we compute AUC given a training set consisting of every link in the network and a probe set consisting of every link in H; this way we can assess the probability that the index assigns a greater score to a random link in H than to a random non-edge. We also consider another standard performance metric, namely the average precision (denoted by AP)38. While this measure is not as intuitive as AUC, it also returns a value between 0 and 1, where 1 means that the links in H are fully exposed , while 0 means that the links in H are fully hidden; see Section A standard way to evaluate a similarity index is as follows. First, the links of the network are divided into a training set, AUC given a training set consisting of every link in the network and a probe set consisting of every link in H; this way we can assess the probability that the index assigns a greater score to a random link in H than to a random non-edge. We also consider an alternative performance metric, namely the average precision (denoted by AP)38.We evaluate the effectiveness of each heuristic against a similarity index in a given network as follows: we run the heuristic iteratively, and after each iteration, we compute using only 5 modifications to a massive telecommunication network, consisting of all 829,725 phone calls between the 248,763 users of a particular European telecom operator, who live in four geographically continuous districts48. Given different similarity indices, Fig. We start by evaluating the effectiveness of our heuristics in hiding 3 edges of a randomly-chosen evader, AUC and AP—while varying the number of nodes, n, and the average degree, d, in Scale-Free networks; see Fig. n and decrease with d. Similar trends were observed when experimenting with Small-World networks and Erdos-Renyi random graphs; see Section Next, we evaluate the attack tolerance of the similarity indices outlined in Theorem 1 based on the two performance metrics—carefully-chosen edges, or whether the same results can be obtained by rewiring any edges in this neighbourhood. With this in mind, we compare the error vs. attack tolerance of the similarity indices outlined in Theorem 1. In particular, we compare the impact of modifying links that are chosen randomly—as is the case in the work of Zhang et al.49—against those that are chosen strategically by one of our heuristics. Here, the random links to be modified are chosen from the same set of links that our heuristics are allowed to modify. The result of adding random links is compared against OTC (which adds links), whereas the results of removing random links is compared against CTR (which removes links). By doing so, the outcome of random modifications can serve as a baseline for evaluating the performance gain achieved when the evader acts strategically. As can be seen in Fig. So far, we have shown that the evader can hide some of her connections by rewiring edges in her network neighbourhood following our heuristics. However, it is still unclear whether these results are due to rewiring Finally, we study how our heuristics affect certain properties of each of the networks outlined in Section optimal way to hide such connections is intractable, the empirical evaluation demonstrates that our heuristics are effective in practice, and reveals that it is more beneficial to focus on “unfriending” carefully-chosen individuals rather than befriending new ones. Our analysis also shows that link prediction algorithms are more susceptible to manipulation in smaller networks and in networks with higher density. Evaluating the error vs. attack tolerance of link prediction algorithms reveals that the choice of connections to modify is critical, since making random choices may actually backfire and end up exposing, rather than hiding, the connections in question.Arguably, hundreds of millions of Facebook users are familiar with the experience of receiving “friend suggestions”, and many recipients of such suggestions are left wondering how Facebook is able to predict relationships that were never disclosed online. These suggestions are guided by link prediction algorithm which, if used with malicious intent, can violate our basic right to choose which of our connections to disclose. Although this privacy concern has already been addressed in the literature, these studies implicitly assume the role of a central authority, while completely neglecting the possibility that members of the social network can themselves mitigate such threats. We fill this gap by developing heuristics that empower the general public, by offering them a readily-applicable way to conceal any connections they deem sensitive, without requiring them to know the topology beyond their network neighbourhood. While we prove that identifying an It should be underlined that the applicability of our heuristics depends on the information available to the evader and the seeker. In particular, while our heuristics can be applied when the evader knows the ego network of the other end of the sensitive relationship, such knowledge cannot be taken for granted, especially if the evader is engaged in a one-sided effort to conceal the relationship. As for the information available to the seeker, it may include not only a snapshot of the social network, but also a history of the modifications made therein. Thus, even if the evader manages to rewire the network and conceal a sensitive relationship, the seeker may still retrieve the original topology, thereby rendering the evader’s actions futile. In such cases, our heuristic may still be implemented by interpreting the removal of a link to mean the avoidance of creating that link, e.g., in our telecommunication example, removing a link may be interpreted as avoiding the corresponding phone call in the first place.All in all, our findings demonstrate that, in an age where people continue to lose ownership of their personal data, individuals can take a stand and at least to some extent shield their relationships from privacy invasion.NG(v) denote the set of neighbours of node v, i.e., NG(v) = {w ∈ V: ∈ E}, and let NG denote the set of common neighbours of v and w, i.e., NG = NG(v) ∩ NG(w). The degree of v will be denoted by dG(v), i.e., dG(v) = |NG(v)|. Whenever it is clear from the context, we will omit the graph subscript, e.g., by writing N(v) instead of NG(v). Now, let Factor 1: the number of common neighbours of the non-edge. More specifically, for every s increases with |N|.Factor 2: the degree of each end of the non-edge, but only if both ends have some common neighbours. Specifically, for every similarity index, s decreases with d(x) and with d(w) if N ≠ ∅.aN = ∅, then s is not affected by d(x) nor by d(w). As for the remaining similarity indices, i.e., those in {sCN, sAA, sRA}, their scores are not affected by d(x) nor by d(w), regardless of whether N = ∅.mentclass2pt{minimFactor 3: the degree of every common neighbour of the non-edge. More specifically, for every similarity index s ∈ {sAA, sRA} and every common neighbour v ∈ N, the score s decreases with d(v). As for the remaining similarity indices, i.e., those in d(v):v ∈ N.Let addition of an edge, , can only affect the scores of the following types of non-edges:Type 1: :x ∈ N(v)\N(w). Such a non-edge is affected by the addition of , which adds v to N, thereby increasing |N|. This, in turn, increases s for every similarity index Factor 1.Type 2: :N ≠ ∅. Such a non-edge is affected by the addition of , which increases d(w). This, in turn, decreases s for every Factor 2.Type 3: : x, y ∈ N(w). Such a non-edge is affected by the addition of , which increases the degree of a common neighbour of x and y, namely w. This, in turn, decreases s for every s ∈ {sAA, sRA}; see Factor 3.Therefore, the x, v) can be of both Type 1 and Type 2 simultaneously; this happens when x ∈ N(w)\N(v) and N ≠ ∅. In this case,  is affected by Factor 1—which increases s—as well as Factor 2—which decreases s. Since these two factor have opposite effects, whether s increases depends on whether the effect of Factor 1 outweighs that of Factor 2.Note that a non-edge  is exactly the opposite to that of adding . For instance, suppose that  is a non-edge of Type 1 and not of Type 2. Then, by adding  to a network increase s for every v, w) from a network decrease s.Finally, note that the impact of removing  ∈ E, where v, w):v is removed from the common neighbours of w and x, thereby reducing |N|. As a result, the similarity score of  decreases according to Factor 1.the node w decreases. As a result, the similarity score of  can only increase according to Factor 2.the degree of node With these observations in mind, let us analyse our heuristics. Recall that CTR removes an edge, , the similarity score of  is subjected to two opposing forces; one that decreases it, and another that increases it, Nevertheless, the following theorem implies that the latter force never outweighs the former one. In other words, by removing , the similarity score of  can only decrease given the similarity indices in In other words, by removing  be a non-edge in G′. Furthermore, let v be a node in G′ such thatand. Finally, let G be the network that results from adding  to G′, i.e., where. Then, for every similarity index, , we have:. v, w) such that, after the addition of :Moving on to OTC, recall that this heuristic adds to the network a non-edge :w increases, which can only decrease the similarity score of  according to Factor 2.the degree of x, w) and  can only increase according to Theorem 2.the similarity scores of  can only decrease the position of  in the similarity-based ranking of all non-edges.Thus, given the similarity indices in Supplementary Materials


The aim of the literature review was to describe how adulthood transition is used in health and welfare.A qualitative design with a deductive approach were used.As material, 283 articles published in scientific journals, between 2011–August 2013, were selected. The search was conducted August 2013. The data were analysed and sorted in a categorization matrix.Transition was identified as a process mainly related to the four types previously identified; developmental, situational, health‐illness and organizational transitions. Another one transition was also identified, lifestyle transition. Health professionals often meet people during transitional periods regarding health and illness Meleis, . In nursChick and Meleis  initiateLater, Meleis, Sawyer, Im, Messias, and Schumacher  identifiEngagement is associated with the level of awareness in relation to the transition and cannot occur if a person is unaware of the transition. Change and difference are other properties of transitions; all transitions include a change, but not all changes are related to transition. Difference implies the perception of being different or seeing the environment in different ways. Regarding time span, Bridges  defined Transition was initially divided into three types: “developmental”, “situational” and “health‐illness transition's” Meleis, , but subIn healthcare contexts, transitions may imply problematic periods for individuals as well as caregivers and occur all‐through the life span. Individuals’ developmental changes from child to adolescent in combination with the change from pediatric to adult health care are critical transitional situations and substantial gaps between healthcare providers have been identified in e.g., diabetes care  NOT .The criteria for inclusion of articles were that they were published between 2011–August 2013: in English; had transition in the heading, abstract, or as a key word; and were related to transition in adult life, from child to adult, or from adult to old age. Exclusion criteria were articles containing conference proceedings, editorials, or letters to the editor.2.3The search resulted in 991 hits and the selection process, ending up in the 283 articles, is shown in Figure 2.4The present literature review aims to describe how adulthood transition is used in the area of health and welfare. It focuses on the concept and therefore considered relevant to include articles varying in design to get information about its use from a broader perspective. Thus, it was not considered relevant to assess the quality of the articles included beyond controlling that they matched the inclusion criteria chosen.2.5The data analysis began with the authors piloting ten articles independently. It resulted in a categorization matrix  were studied more in detail, using an inductive approach, to analyse its content.2.7Patient consent and ethical approval was not required for the literature review.3lifestyle matters in one way or another, good or bad, ending up in the identification of a “new” type of transition, called “lifestyle transitions”.The analysis showed that most articles could be related to the four types of transition previously identified by Schumacher and Meleis  developm3.1All four types of transition identified by Schumacher and Meleis  were see3.2The new type of transition identified, not previously mentioned by Schumacher and Meleis  was the Physical activity referred to participation in fitness related activities (Patel et al., Transitions regarding tobacco use were found in relation to nicotine dependence (Kushner, Menary, Maurer, & Thuras, The lifestyle matters identified had either a positive direction, from unhealthy to healthier behaviours (Boothe et al., 4This literature review aimed to describe how adulthood transition is used in the area of health and welfare. The results demonstrated that transition remains a frequently used concept, as reflected in the great number of articles included. Our results also show that the concept mainly is used in line with the types previously identified Meleis, . HoweverAfter the year 2000, it has been an emerging emphasis on the individual's responsibility for maintaining health through lifestyle choices (Ahola‐Launonen, The importance of awareness of transitions can manifest itself in various ways. On an individual level it can be exemplified as transitions of unrepresented patients who lack decision‐making capacity and are in need of an advocate, which is a problem in many healthcare situations (Abdool et al., 4.1The discovery of a new type of transition, lifestyle transition, could be regarded as our main finding, but, it cannot be ruled out that something might have been missed. Sometimes, it was also difficult to decide which type a certain transition was and this may reflect the time that has passed since they were originally identified (Schumacher & Meleis, The present analysis also included many articles from different areas in health and welfare which is a strength considering the possibility to include “all current use”. Additionally, the authors’ professional experiences from different fields of health care also contributed to secure rigor in the literature review.4.2There are also limitations with the present study. Firstly, solely including articles from the years 2011–2013 could be considered a limitation; but was necessary regarding the number of articles containing the concept. The decision not to include articles published later than 2013 is, potentially, a more severe limitation, but a product of a long process with analysing the results. Another option would have been to make new searches with an extended timeline. However, this would imply another limitation in the literature search since the publication date and the time update of a database is not necessarily the same. Verification of this, together with new analyses, would be very time consuming and we decided not to do that. Another potential limitation, but also an asset concerning inter‐reliability, was the fact that there were several researchers included in the analysis.5This literature review aimed to describe how the concept transition is used in the area of health and welfare in adulthood. The results showed that most of the uses of transition belong to the previous identified types: developmental, situational, health‐illness and organizational transitions. Additionally, another type of transition was also identified, the lifestyle transition. Awareness of life style transitions and their complexity is of importance on a societal level as well as among healthcare professionals to support individuals to manage behavioural changes regarding lifestyle matters.No conflict of interest has been declared by the authors.