Assuming that potential biases of Artificial Intelligence (AI)-based systems can be identified and controlled for , employing such systems to augment human resource (HR)-decision makers in candidate selection provides an opportunity to make selection processes more objective. However, as the final hiring decision is likely to remain with humans, prevalent human biases could still cause discrimination. This work investigates the impact of an AI-based system’s candidate recommendations on humans’ hiring decisions and how this relation could be moderated by an Explainable AI (XAI) approach. We used a self-developed platform and conducted an online experiment with 194 participants. Our quantitative and qualitative findings suggest that the recommendations of an AI-based system can reduce discrimination against older and female candidates but appear to cause fewer selections of foreign-race candidates. Contrary to our expectations, the same XAI approach moderated these effects differently depending on the context.The online version contains supplementary material available at 10.1007/s12525-022-00600-9. AI comprises a variety of methods, such as machine learning (ML) and rule-based symbolic logic, which differ in their complexity and suitability for different tasks  in hiring decisions?Yet, AI-based systems are not likely to entirely replace humans in hiring soon but rather to augment human decision-making  seeks to provide better insight into how and why an AI-based system operates the way it does . As discrimination can differ between countries, we focused on a specific country and chose a German context for our study.The remainder of the paper is structured as follows: First, we review relevant literature on AI-based systems in hiring, biases, and discrimination in hiring processes, and XAI. In the methods section, we describe the sample, the development of the prototypical AI-based system for candidate management, and the employed questionnaires. We then present quantitative and qualitative insights from our study and discuss them in the context of the relevant literature. The paper concludes with limitations, opportunities for future research, and a short summary of the main findings.As previously mentioned, AI is the frontier of computational advancements and refers to machines performing a spectrum of cognitive tasks commonly associated with human intelligence, such as complex decision-making  reasons for selecting a candidate, especially if the candidate preferred by the human and the recommendation of the AI-based system differ  scale to assess the tendency to actively engage in intensive technology interactions. The scale requires participants to rate their agreement with statements such as “I like testing the functions of new technical systems.” We included a definition of “technical systems” to ensure a common understanding. Additionally, participants were asked to answer the Human Computer Trust Scale (HCTS), which was adapted to AI and the context of hiring; for example, “I think that Artificial Intelligence is competent and effective in selecting candidates.” To ensure a common understanding of AI, we included a definition describing AI as “a system that can adapt independently to new situations and contents. It can solve problems and tasks that require a certain level of intelligence, as is typically present in humans.” To measure the subjective cognitive load after interacting with the candidate management platform, we included the NASA Task Load Index (NASA TLX), consisting of questions such as “How mentally demanding was the task?” We excluded the scales for physical and temporal demand as those were not relevant for this study. The Ethics Position Question (EPQ) was used to measure ethical dispositions by asking for agreement to items such as “Risks to another should never be tolerated, irrespective of how small the risks might be.” It was included last to avoid priming ethical behavior in the decision task. For all questionnaires, German translations or existing German versions were used, and all items were measured with a 7-point Likert scale. Following Oppenheimer et al. , manipulFirst, participants received general information about the study and data protection and were asked to provide their written consent. It was specified that the study could only be completed on a desktop or laptop computer, and participants could not proceed if another device was used. Afterwards, participants were asked to answer the demographics, BFI-10, ATI, and HCTS questionnaires Table  and wereIn the experimental groups with XAI-induced transparency , candidate selection view Fig. , and scrThe “job view” of the platform showed four job advertisements, for three of which the participants should select candidates . The job advertisements were identical for all participants but displayed in a randomized order. The participants were free to decide which job advertisement to start with. Each job advertisement was accompanied by a short description of the job. This description included references to different qualifications and was provided to ensure a common baseline for the participants’ assessment of the candidate’s qualifications.After participants selected one of the three job advertisements by clicking on “Start Selection,” an animated loading screen appeared that served to simulate the AI-based system’s selection process Fig. .Fig. 3LoNext, the participants saw the “candidate selection view” . A complete list of candidates is included in Table Only in experimental groups 3 and 4 were specific candidates recommended to the participant by the AI. Specifically, in the three relevant rounds per job, the candidate with a sensitive attribute was labeled with “AI Recommendation” in the upper right corner and the “invite applicant” button was complemented with small arrows  for their decision after each choice. The cover story explained this to the participants by pointing out that supervisors wanted to track the reasons behind the decisions. Once the participants had completed all three job advertisements, they were directed back to the online survey. A complete overview of the order of the questionnaires, the content presented, and the information collected in this study is provided in Fig. Individuals above the age of 18 years were eligible to participate in this study, and participants were recruited though SurveyCircle. Further restrictions for participation were not imposed. SurveyCircle is a research platform that helps European researchers recruit participants for online surveys and experiments. SurveyCircle’s idea is to provide the opportunity to experience current online studies and actively support research in different disciplines through voluntary participation. As with SurveyCircle a completely representative sample cannot be guaranteed, we reached out to additional participants via postings on LinkedIn, XING, and Facebook. It would be obvious to limit participation to people working in HR. However, we found that even among HR employees and within strategic HR management there are many differences between systems and HR philosophies , of whom 126 were women (~ 65%) and 68 men. This approximates the real distribution of male and female employees working in HR in Germany, which is around 70% women and 30% men  and employees , there was an age difference of almost 9 years. Moreover, among the employees, 68% reported having a university degree. Nearly one-third of the participants stated that they had experience in HR.A total of 208 participants took part in the study. With 14 participants excluded for not providing reasons for their candidate selections on the interaction platform, 194 valid cases were included in the analysis. At the end of the survey, participants were asked whether they answered all the information honestly and in the best interests of the scenario (the participants were assured that their answer to this question would not put them at a disadvantage). No additional participants were excluded on the basis of this question. On average, participants spent 29 min completing the study. Participants ranged in age from 18 to 68 years , unpaired The relevant candidates with sensitive attributes (in terms of diversity) were coded 1, and the rest 0. The scores in Table We then assigned a score for each participant per job. The values in Table M = 1.98, SD = 0.948) than those without recommendations . There was no statistically significant difference between the candidate selection with recommendations and the group without recommendations, t(96) = 0.722, p = 0.472, r = 0.075. Thus, the first hypothesis was not supported. There was no significant effect of an AI-based system’s recommendations on the selection of foreign-race candidates.Regarding race (H1), participants who received the AI-based system’s recommendations were less likely to select foreign-race candidates  than those without recommendations . There was a statistically significant difference between the candidate selections with recommendations and the group without recommendations, t(96) = -2.555, p = 0.012. The effect size is r = 0.251 and corresponds, according to Funder and Ozer (Regarding age (H2), participants who received recommendations from the AI-based system were more likely to select older candidates  than those without recommendations . The Levene test did not show homogeneity of variance (p < 0.5). Therefore, the Welch test was conducted. There was a statistically significant difference between the candidate selections with recommendations and the group without recommendations, t(88.711) = -2.202, p = 0.03. The effect size is r = 0.214 and corresponds, again according to Funder and Ozer (Regarding gender (H3), participants who received the AI-based system’s recommendations were more likely to select female candidates   or XAI .To analyze whether the interaction between XAI and the AI-based system’s recommendations significantly predicted participants’ candidate selections, moderation analyses using the PROCESS macro by Hayes  were conF = 5.46, p = 0.001, predicting 6.88% of the variance. The moderation analysis showed that XAI significantly moderated the effect between the AI-based system’s recommendation and the selection of foreign-race candidates: ΔR2 = 4.66%, F = 9.33, p = 0.002, 95% CI. Thus, Hypothesis 4.1 was confirmed.The overall model regarding the selection of foreign-race candidates was significant F = 4.04, p = 0.008, predicting 5.8% of the variance. However, the moderation analysis did not show that XAI significantly moderated the effect between the AI-based system’s recommendation and the selection of older candidates: ΔR2 < 0.01%, F = 0.084, p = 0.772, 95% CI. Hypothesis 4.2 was not confirmed.The overall model regarding the selection of older candidates was significant F = 4.96, p = 0.002, predicting 7.72% of the variance. The moderation analysis did not show that XAI significantly moderated the effect between the AI-enabled candidate recommendation system and the selection of female candidates: ΔR2 < 0.01%, F = 0.587, p = 0.444, 95% CI. Hypothesis 4.3 was not confirmed.The overall model regarding the selection of female candidates was significant As only one sub-hypothesis showed significance, Hypothesis 4, which states that XAI moderates the effect between an AI-based system’s recommendations and candidate selections, could not be confirmed.To summarize the findings, Fig. The participants were asked the following question after each selection: “Why did you select this candidate?” Their reasons for selecting candidates on the platform is evaluated in the following sections. The dataset consists of 1,746 fields (194 participants × 3 jobs × 3 reasons), including keywords, sentences, and short argumentations. To gain insights into the reasons for selections, we conducted qualitative content analysis according to Mayring .Content analysis allowed us to summarize and reduce the participants’ reasons to their essential message. The coding categories were derived inductively and included the five different qualifications of the candidates  and a general qualification category for those cases in which the participants did not specify which qualification contributed to the decision. Furthermore, the categories included the three sensitive attributes , each with the sub-categories of “sensitive attribute preferred,” “non-sensitive attribute preferred,” or “unspecified.” Additionally, we included a category for coding whether the participants mentioned that they followed the AI recommendation. Lastly, we included the categories “subjective” for personal reasons, “ethical” for general comments about diversity without a specific reference to race, age, or gender, and “excluded” for comments that did not specify any reasons. The coding was conducted by two of the authors, who discussed the coding at several points in the process to decide on ambiguous cases. Comments could be assigned to multiple categories. Table Regarding the percentages for the sensitive attributes of race, age, and gender, all mentions of these attributes in the comments were considered. Thus, the percentage displayed includes all comments referring to a sensitive attribute, regardless of whether these are positively or negatively framed. This was done because considering these sensitive attributes in a hiring process could already be seen as a form of discrimination. For an explorative comparison, Table For the first job, “IT-Administration,” approximately half the participants mentioned “programming” as a reason for selecting candidates. For the other jobs, the reasons for selection were more evenly distributed across different skills. Furthermore, while race and gender were rarely explicitly mentioned in the comments, relatively more comments addressed the age of the participants. Interestingly, more participants with HR experience mentioned age than those without HR experience. Lastly, it is interesting that participants with HR experience mentioned the AI recommendation less frequently as a reason for their selection compared to participants without HR experience.We also examined the comments on race, age, and gender in more detail. All but one comment coded in the gender category expressed a preference for female candidates, mentioning, for example, the quota for women , that in the case of similar qualifications, women should be preferred (ID 1739), that women should be supported in certain disciplines, such as IT (ID 1848), or that a “woman is always good for team morale” (ID 1492).With regard to race, several participants expressed a preference for non-Turkish candidates, for example, “I would not invite a Turk” (ID 1405) or “Turkish, but still more IT experience” (ID 1492) or stated “German” as the only justification for their candidate selection (ID 1385). However, one participant emphasized positive aspects of increasing diversity with new hires, stating “intercultural, therefore access to other resources” (ID 1749).Lastly, almost all participants who commented on a candidate’s age preferred younger candidates, stating, for example, that another candidate has “more time before retiring” (ID 1395). Several participants connected age to the ability to learn quickly, which compensated for methodological skills that they were currently lacking. For example, “methodological competence can possibly be further developed due to his age” (ID 1692). The only pro-older candidate comment was “Older women should be supported” (ID 1848)..The participants’ selection of foreign-race candidates was not in line with existing literature on racial discrimination in hiring, which has found that race continues to be one of the main sources of discrimination in hiring today and that Turkish individuals are especially discriminated against in Germany Baert, . This waWe suggest that the implemented AI recommendation did not work as assumed because of algorithmic aversion  was identical for the candidates, the individual scores for qualifications differed slightly. However, it is also possible that participants were not aware of or avoided mentioning the role of the candidate’s race in their selection, either because they were not aware of their own biases or because they did not want to admit them .Examining the participants’ reasons in more detail, we found that programming experience was the most frequently mentioned reason for the decision in the first job round in which race was the sensitive attribute. We therefore assume that the reason for not finding an effect of AI recommendation on selecting foreign-race candidates could be that the majority of our participants were sure that programming skills comprised the most important criterion for the role of IT administrator even though other skills were mentioned in the job description. Thus, an AI recommendation does not seem to be effective when there is already a clear qualification-based indicator for a decision. This could be explained by a high level of confidence that results in the avoidance of following AI recommendations. The result of the personality test (BFI-10) could also be used to explain the lack of evidence for discrimination in the initial candidate selection. A high level of openness  can be further strengthened by the qualitative results, which reveal that 13% and 17%, respectively, of the participants who received recommendations mentioned it as a reason for their candidate selection. In addition, participants stated that they used the recommendations to make decisions in cases of uncertainty. Regarding the second hypothesis, where we considered whether an AI-based system’s recommendations impact the selection of older candidates, it was supported and showed a medium-sized effect. This implies that the recommendations led to more frequent selection of older candidates. These findings are strengthened by the qualitative findings, in which the participants mentioned the recommendation as a reason for their selection. Furthermore, the participants’ ethical position (EPQ) indicated that they possessed rational and diverse candidate selection behavior. The findings for the sensitive attribute age (H2) are also in line with current literature regarding discrimination, which shows that older candidates are subject to discrimination in hiring Baert, . In summComparing the participants with and without HR experience, those with HR experience mentioned age more often as a reason for their decision. If we assume that a candidate was selected mainly because of their age and not because of a certain skillset, we consider this a case of age discrimination Baert, . HoweverOur findings suggest that XAI-induced transparency, that is, providing participants information about the functionality of the AI-based system, did not moderate the effect of the system’s recommendations on the selection of older and female candidates (H4.2 and H4.3 rejected). It appears that emulating the processing of an AI-based system by providing a high-level explanation of the input–output relation of the data did not – as would be expected based on the suggestions of Gilpin et al.  – increaWith these findings, we addressed the research gap identified by Adadi and Berrada , who argThe study adopted a fairly broad, high-level type of XAI in which participants received a general explanation about the processing of data in the system as well as its goal of augmenting decision-making in hiring to reduce discrimination. However, there are many other, more technically detailed XAI approaches that could prove (more) effective in this context  was identical for both candidates in each round; the star ratings for specific qualifications differed between the two candidates. While this was necessary to gain insights on participants’ tendency to consider demographic information for deciding between candidates, it introduced the risk that the participants’ perceived relevance of certain qualifications for a job influenced their selection. Also, the effects might differ if participants had to select candidates from a larger pool of candidates on the platform rather than making a choice based on a direct comparison of two candidates. Lastly, the cultural context in which the platform is deployed might make a difference as for example the influence of AI recommendations could be more pronounced in highly technology-affine societies.Addressing these limitations, future research could explore the (dis-)advantages of different types of XAI from the perspective of HR professionals in greater depth. It would be interesting to conduct the study in a real HR environment and limit participation to experienced HR employees. As the sensitive attributes leading to discrimination might differ depending on contextual factors  or individual factors , future studies should aim to explore the effects of AI recommendations and XAI with different sensitive attributes  and a diverse group of HR professionals. Furthermore, to dive deeper into possible causes for the observed candidate selection behavior and the effectiveness of AI recommendations and XAI, future research could measure algorithmic aversion, automation bias, cognitive load, or the effect of mistrust disposition. Future research should consider directly measuring these aspects in the context of XAI and AI recommendations for candidate selection and examine possibilities to mitigate aversion, for example, by incorporating AI-based information systems as part of new task designs that balance human and systemic characteristics through mutual delegation and through organizational learning processes with strong stakeholder participation in AI adoption. Additionally, to improve generalizability, future research could investigate XAI and AI recommendations on different types of candidate management platforms and in alternative deployment contexts .Lastly, we focused on the point of view of the recruiter and not on those who are affected by discrimination or bias in hiring. Future research needs to go a step further and, for example, follow a discourse ethics approach based on that of Mingers and Walsham  by also The findings of this study contribute to research on augmenting human decision-making with AI-based systems in several ways. First, we showed that in decision-making scenarios with no clearly preferable option, providing AI recommendations and XAI can influence decision-making and potentially reduce discrimination in hiring. Second, our findings suggest that a clear association between a qualification-based criterion and a decision outcome limits the impact of AI recommendations on decision-making. Third, our exploratory analysis indicated that participants with domain knowledge did not behave differently in response to AI recommendations and/or XAI than participants without domain knowledge. Fourth, we open a new field of research regarding the combination of XAI and AI-based system recommendations to augment decision-making in the context of hiring.We also contribute to the literature on XAI by empirically testing the influence of XAI on the effectiveness of augmenting decision making with an AI-based system in the context of hiring. As the effects of XAI differed for the sensitive attributes, our findings suggest that, in addition to quality criteria and target groups  biases, and better understand the reasons for AI-based system recommendations through XAI. On the other hand, this research can draw the attention of organizations and AI managers to the issue of discrimination remaining an important problem in hiring. Furthermore, the platform conceptualized and developed in this research can be a starting point for developing a system for training HR staff on discrimination in hiring. XAI and recommendations from AI-based systems can be effective, but they may require further action from the organization to achieve diverse hiring in the long term.For practical application purposes, XAI might be successful in areas where users are in more frequent contact with the technology. Complementing XAI, the implementation of AI recommendations might be a suitable method to realize the AI’s purpose of, in this case, countering discrimination in hiring. Moreover, in general, educating different stakeholders of XAI about AI’s potential dangers and benefits would be advisable to reduce prejudice and fear and increase general acceptance of AI.From an organizational perspective, a question arises as to the overall benefits of XAI for their business. Not every organization that uses an AI-based system necessarily needs to understand the reasons for its outcomes. In addition, some algorithms must be developed from scratch to allow for the ability to explain the processes and reasons for decisions afterward. This leads to an immense amount of work, which may not justify the perceived benefits of XAI in every context. Therefore, incentives are needed that could counteract some of the barriers to the implementation of XAI to ensure more diversity and fewer biases through XAI and AI based-system decisions and recommendations.In summary, our findings suggest how recommendations by an AI-based system for hiring, combined with an XAI approach, can be applied on a candidate management platform to achieve greater transparency and diversity. It appears that AI recommendations are sufficient to cause participants to reconsider their decision-making or to draw attention to sensitive attributes. While our findings might not generalize to other AI-based systems or candidate management platforms, we found that AI recommendations encouraged decision makers to select more female and older candidates. However, the recommendations also resulted in fewer selections of foreign-race candidates, which might be due to algorithmic aversion caused by overly obvious recommendations based on sensitive attributes. Furthermore, while explainability moderated the effect of AI recommendations on the selection of foreign-race candidates, our findings cannot unreservedly support the positive impact of explainability on the effect of AI recommendations on selection behavior. However, our findings overall suggest that AI recommendations can reduce discrimination in hiring decisions. We further conclude that XAI helped reduce reactance and aversion caused by recommendations that were too obviously perceived as an influencing factor by our participants. The XAI appeared to have different effects for the same target group and the same quality criteria, highlighting the importance of considering the content of the decision task.Supplementary file1 (PDF 483 KB)Below is the link to the electronic supplementary material.


In response to the COVID-19 pandemic, elite sport leagues implemented hubs, or ‘bubbles’, which restricted athletes’ movements and social interactions in order to minimise the risk of athlete infection and allow competitions to continue. This was a new way of working and living for elite athletes and there was a dearth of literature on this topic. The main objective of the study was to investigate the impacts of the hub model on athletes over time, and what job demands and resources existed for athletes through the application of Demerouti et al. (2001) Job Demands-Resources Model. Multiple sequential semi-structured interviews were conducted with Women’s National Basketball League (WNBL) athletes during the 2020 season, which was held entirely in a hub in North Queensland, Australia. The key job demands in an elite sport hub identified were the volume of work, simultaneous overload and underload, and nature of work in the hub. The key resources that emerged include recovery services, control and player agency, and constructive social relations. Despite the presence of job resources, which work to counteract, or buffer job demands in order to reduce work stress and improve motivation, they were found to be insufficient for athletes and inequitably distributed between clubs. The intensity of the hub model also amplified demands present in all WNBL seasons. This research is therefore useful for planning of future elite sport leagues to improve the type and amount of resources available to athletes, thereby improving athlete wellbeing and performance both within and outside a hub model. In response to the coronavirus COVID-19) pandemic, elite sporting competitions had to adapt quickly to survive. During the initial stages of the pandemic, sport was cancelled or postponed in many cases, from grass roots community competitions to major international events such as the 2020 Tokyo Olympics 9 pandemi. Many atThe Covid-19 pandemic impacted athletes’ training and competition, affecting their fitness and skill maintenance and development, as well as their mental health . In sporBasketball Australia, the national sporting organization representing basketball in Australia, implemented a hub model for the entirety of its professional Women’s National Basketball League (WNBL). All eight WNBL teams relocated to Queensland for the duration of a condensed six-week season (compared to the previous year’s sixteen-week season) between the 12th of November and the 20th of December 2020 . Games aThe WNBL’s 2020 season presented a unique and unprecedented opportunity to examine the impact of the hub model on elite female athletes, including the demands of competing during a global pandemic, being away from home for the duration of a season, living with team members, coaches and support staff full-time and the physical and emotional stressors of an intense playing schedule. The hub-based season also presented an opportunity to explore the inherent strengths and limitations of the regular non-pandemic competition through the lens of the players’ hub experience.Furthermore, the impact of COVID-19 and associated restrictions on athlete wellbeing is not well documented. In their recent study, Defreese and Smith  found thIn order to determine the impact of the hub on athlete wellbeing, performance and burnout, it is important to identify the stressors or demands that exist for athletes in the hub environment, and what supports are most useful to help athletes meet those demands. In this study, we employ the Job Demands-Resources (JD-R) model , 21, as Demerouti et al.  posited Empirical studies show that jobs with high demands often deplete employees’ physical and mental resources, resulting in exhaustion and poor health outcomes. In a study of Dutch call center workers, Bakker, Demerouti and Schaufeli  found thIncreased stress and decreased motivation in athletes have been linked to experiences of burnout, which in turn can lead to reduced athletic performance, and impaired physical and psychological health . The JD-By developing an understanding of the job demands that exist in the hub and which resources are useful to counteract them, this research will provide an understanding of how the hub impacts on athletes over time. This study used qualitative data from multiple, sequential semi-structured interviews to understand WNBL player’s perceptions of the hub over time, how their wellbeing and performance was affected, and whether feelings of burnout were experienced.All 96 WNBL athletes playing in the 2020 season were invited to participate in this study by a senior staff member of Basketball Australia. Prior to recruitment, he Human Research Ethics Committee approved this study, number HEC20458, and at the beginning of each participant’s first interview, oral consent was obtained. We conducted 20 semi-structured interviews with nine athletes playing in the 2020 WNBL season (interviewing approximately one in every 10 athletes). Players were asked to express their interest in participating in the study by completing an online expression of interest (EOI) form. Eleven athletes completed the EOI, who were contacted and provided with additional information about the study. Nine athletes agreed to participate in the study and their demographic characteristics are presented in To explore the impact of the hub on athlete wellbeing and performance, multiple, sequential interviews were held with each player during the 6-week season . This approach was applied as it was anticipated that the athlete experience would change over the course of the hub and this would foster richer insights. Three athletes participated in three interviews each, five athletes participated in two interviews each, and one athlete participated in one interview.Interviews were conducted over the phone (n = 18) or via video conferencing (n = 2), depending on the athlete’s preference. On average, each interview was 30 minutes duration, which, with the multiple sequential method and considering the limited time athletes had available away from their basketball duties and recovery time, allowed data saturation to be reached and no new themes emerged in the third interviews with participants. Interviewees were asked a range of questions about their experience of being an elite athlete in a hub environment and how it impacted their wellbeing, as well as what they were finding demanding in the hub, and what resources they accessed or were aware of to support them during the competition. Refer to Appendix 1 for the full interview guide.The interviews were recorded and transcribed verbatim immediately following each interview. The transcript of each interview was returned to the interviewee for verification. Pseudonyms were used to ensure the anonymity of the athletes. The data was then imported into NVIVO 12 for analysis. The data was analyzed thematically, using the approach outlined by Braun and Clarke . ThematiThe results are presented in three sections. The first focuses on the longitudinal data and how the athletes’ experiences of the hub changed over the course of the six-week season through a vignette of one player’s experience. The second section covers the job demands experienced by athletes, and the third addresses the job resources that were available in the hub.By undertaking multiple sequential interviews with participants, we gained an understanding of how the hub life impacted participants over time, and explored whether experiences of burnout intensifies the longer the individual was situated in that environment. Due to the condensed nature of the season, not all participants were able to find time for three interviews over the six weeks. Player 4 participated in all three interviews  and their experience is highlighted as a vignette.In their first interview, Player 4 was already discussing some challenges in the hub, particularly being constantly surrounded by their teammates, but overall was positive about how it was working and the steps they were taking to detach from the sport and make sure the team atmosphere was positive:I’d say 75% doing well. Then you’re going to have to struggle, have your ups and downs, but majority of it has been good… We tried to keep our days off real to ourselves because we don’t want… We know we’re going to be on top of each other all the time so we really try to limit team stuff. (Player 4.1)Just after the halfway point in week four, Player 4 experienced ‘time dragging on’ and talked about the difficulty of being away from home and being unable to help with family issues. This negatively impacted their wellbeing and was different to a regular season where they would be spending the majority of time at home surrounded by social support structures:Everything’s kind of almost compacting. There’s a lot of family stuff going on for me back home and yeah, it’s just been a bit harder to manage, and I’ve had some consecutive bad days, which it’s not easy, but yeah, trying to figure it all out… it was just a bit more tough I would say, this last week. (Player 4.2)By the last week of the competition, Player 4 had lost motivation and wanted to go home. They experienced some challenges communicating with their coach and poor team performances which they found frustrating:I’m definitely over it. I don’t know. It’s been hard. Since last time we spoke, it’s been more challenging for sure… just trying to get through it, to be honest at this point. (Player 4.3)Player 4’s team also did not have a full-time physiotherapist employed in the hub, so they had limited physiotherapy appointments for recovery. By the end of the season Player 4 discussed the result of this, and the negative impact the hub and lack of physiotherapy was having on her physical wellbeing:I think my body is just a bit worn down. The other day we had physio, but we only had a two-hour block for it. So it was girls that play top minutes [who] had the priority of the sessions. Since that was the game I played two minutes, that wasn’t me. Then I was like, "I need to see the physio”. My back is quite sore just from getting hit all the time. (Player 4.3)By examining this participant’s responses to the same questions at different time periods we gained insights into the athlete’s mental and physical wellbeing, their outlook, and their motivation to be in the hub, which all decreased over time. This was similarly evident in other athletes who participated in two or three interviews. Themes also began to emerge in the data, and these are explored in the thematic analysis of job demands and job resources below.The concept of demands refers to characteristics of the hub that participants perceived to be difficult or requiring physical, emotional or cognitive effort, likely adding to the decreased motivation over time, as noted above. The demands that emerged from the analysis and that will be unpacked in the following section are: volume of work, simultaneous overload and underload, and nature of work.Athletes in the hub had to undertake a high volume of basketball-related work in a condensed season, which included up to four games per week. Training, active recovery, whiteboard and film sessions, as well as game preparation before each game, were also activities athletes had to engage in. In some teams, players who did not play many minutes during a game reportedly also had to complete ‘top-up’ fitness sessions the next day, in addition to the full team training, in order to maintain their conditioning. During a typical basketball season, players said they would usually compete in one game per week and all other sessions would be evenly spread over several days, affording more rest time for athletes and the opportunity to fit in other pursuits. Therefore, the hub created a high workload in a condensed amount of time, which led to physical and psychological overload for all participants, evidenced in the quote below:We have a bunch of meetings because that’s the way we get better, it’s because we have to have game film because we can’t get on the court, and so they tried to pinch everything together because before it was like, okay, we’d have physio at this time. And then an hour and a half later we’d have a meeting and then an hour and a half later we’d have to go team recovery. So even though we didn’t have training the whole day, we were going the whole day and it was just so much, it was so much. (Player 3.2)Interestingly, despite this experience of a high volume of basketball-related work, some participants discussed experiencing underload whilst in the hub. When participants were not training, playing, recoverin or preparing for games, there were limited options available to take their mind off basketball, keep them occupied, or ‘detach’. While there were break times scheduled, participants found it hard to use them to relax or wind down:There is time for breaks. However, I think because of the environment that we’re in, I constantly feel like I’m on edge. So, I’m constantly waiting for what you have to do next or where we need to go. And I feel like we’re constantly moving. (Player 8.1)In addition to the inability to ‘detach’ caused by a lack of activities away from basketball, participants reported a lack of time on the basketball court to get ‘shots up’ and get their ‘eye in’. There were limited basketball courts in the hub cities that teams could access, and they were rostered between all teams for formal training. Therefore, participants were only allowed on a court during rostered training times and were unable to access a court during their free time to have individual ‘shoot-arounds’ with the court to themselves. Many participants use shooting practice to relax and build confidence. Losing this crucial outlet disrupted routines, worsened the boredom and added to the emotional demands of the hub:You just miss out on getting their little touch shots that we would normally have. I think that’s disrupting a lot of players. Players who have routines… been thrown in the deep end, but there’s no way you can do a normal routine. So, some people really struggle with that. (Player 5.1)Some participants described an interesting dichotomy of experiencing both role overload and role underload throughout their stay in the hub. Player 7.1 lamented:It’s really frustrating because when we are not at training, I’m just sitting around twiddling my thumbs or getting into YouTube holes. But then, if we play for under 10 minutes (in a game), we have to do an extra workout session for that day. (Player 7.1)This highlights the lack of stimulation and under-utilization of players’ skills and abilities away from the court on the one hand, and the role overload experienced by the same player, having to engage with the intensity of match day and travel requirements and then being required to complete additional training to maintain their match fitness should they not receive enough game time.This experience of competing demands was also echoed by Player 9.1 who described having ‘a lot of time for breaks…. It’s a bit too much’ while also recounting the physical exhaustion she experienced from the condensed calendar:[Our] bodies are sorer than in a normal season when you’ve got seven days to recover after a game. [In the hub], you’re playing two sometimes three games per week, travelling, training every day, lifting every day. (Player 9.1)Athletes were removed from their usual support structures and routines in the hub, which at home would often reportedly include low intensity shooting sessions. This was summed up by Player 9.1: ‘The people that don’t play much, we haven’t had any time on court to shoot, which has been a bit of a struggle. I wish we had had more time’. In the hub, because of the extra physical demands of the condensed season, as well as a lack of courts available, participants either experienced periods of high intensity of periods of boredom and there was a lack of of relaxed basketball opportunities which could have filled the athletes’ downtime and allowed them to maintain their routine without adding physical load.As well as the amount of work, the nature of the work participants undertook in the hub was also demanding. Several characteristics of the hub added to the demands that participants reported experiencing and brought to light issues that may not have been as evident during a regular season.All participants expressed the feeling that uncertainty and a lack of routine was inherent in the hub, as referred to above. Furthermore, schedules were often set at the last minute, with athletes having limited advance notice of when they were training or scheduled for other team activities.We generally know when we’re going to have a training, when we’re going to have games and shoot around, but it’s all of the other meetings in between. It’s film sessions, or just a team meeting, or something like that will generally get dropped on us the night beforehand, so it makes it hard to plan things. (Player 6.2)Athletes’ pre-game routines were also severely disrupted. In addition to the impact of a lack of courts for training discussed above, three athletes discussed their on-court warm-up being cut from up to two hours to 20 minutes if their team was playing in the second game of the night. For these participants, this was a key part of their routine and confidence building before the game, and they found losing it cognitively demanding:It’s that second game… We walked out and we had 18 minutes to do warm up, and it just was so rushed. Yeah. That was pretty challenging. Obviously, on a normal WNBL season, you get out there at the game two hours before, you just get some shots up, get some ball handling, get your mind in the game. (Player 2.1)Staying in hotels with teammates for the duration of the season was also a significant change compared to previous seasons when participants reported that they would usually stay at home except for occasional overnight trips to away games. As a result, participants were unable to get a break, not only from their basketball work but also from their teammates. This was a significant demand that was emotionally draining and exhausting for many of the participants:I will say that one thing that I found difficult is, I’m the type of person who really needs to… I’m definitely an extrovert, but I’m also an introvert as in, I get my energy from having my own time alone and stuff… It felt like I didn’t have any time on my own. When I finally got back to my apartment, it was time to go to sleep. And so, I had no time to decompress. (Player 3.1)Finally, the hub environment that the athletes were working in was often stressful or physically uncomfortable, which increased the demands on the participants. Uncomfortable beds, in combination with late games that were hard to wind down from, led participants to report difficulty sleeping. This chronic sense of tiredness negatively impacted participants’ emotional state and their capacity to deal with the demands of the hub, particularly towards the end of the season. Consequently, participants described feeling emotionally drained by the end of the season and a desire for the season to be over.This section presents the job resources that interview participants reported were helpful to reduce job demands and improve their wellbeing and performance in the hub. An important caveat to these resources was that although they existed, many were not equally efficacious or equitably distributed across individuals or teams, and all participants reported decreased wellbeing or reduced motivation towards the end of the hub, indicating the resources were not sufficient to counteract the significant demands reported above. The resources identified by participants are based around the following four themes: recovery services; control and player agency; role clarity; and constructive social relations.Several support services were provided to the WNBL players by Basketball Australia (BA) and the Player’s Association (PA), including the physical recovery centers, ice baths and massage therapists that all participants reported being able to access. However, participants did not report accessing them equally and many would have liked to access them more. Some participants reportedly did not use them, finding it hard to fit into their busy schedule (as outlined above), or found the services provided were unsuitable. Furthermore, participants disclosed that often the ice baths were not cold enough or there was only enough ice for one team. Massages were available in the recovery centers but participants that were older and had more experience in the WNBL asserted the need for longer and more frequent massages to help them recover more effectively. Participants reported that the recovery services provided were insufficient to counter the physical demands of the condensed season:We went there [to the recovery center] and checked it out and it was just really basic and stuff we could do here. And I think we were told it was going to be something like plunge pools and stuff and it hasn’t been that. So, we’re like, "Okay, we’ll just do pool recovery and get on a foam roller," kind of thing… In Townsville, they had ice baths, so we were using those after games. Here, they are providing one ice bath at the game. But the last game we played, the team before used all the ice, so we couldn’t have one. (Player 5.1)Clubs also provided some recovery services to their athletes, in particular physiotherapists. However, the volume and availability of physiotherapists reported by participants differed between the teams, which was likely due to the financial position and different ownership structures of the teams. Some participants discussed that their club had a physiotherapist travelling with the team for the duration of the hub to prevent and manage injuries and recovery, and participants from one club had their own massage therapists available after every game, while other clubs did not have a dedicated physiotherapist travelling with them so they had to outsource physios from local clinics for limited periods each day. This was an inequitable distribution of resources that facilitated greater recovery opportunities for some players/teams who could access physiotherapy whenever they needed rather than at set times, allowing them to cope better physically with the demands of the season. Furthermore, in the hub, participants discussed being more aware of the inequity between teams because it was more obvious when teams were travelling together and could observe these resources in action:When we played [team] the other day, we got back, some of us hopped in the pool to do recovery, and they’ve got the physios and massage people all there. So, there was probably four of them on a bed getting massages. And I was just like, "God." And like they would have done that throughout the whole team. Whoever needed it, it wouldn’t just be based on how many minutes you’re playing and things like that. It’s just we did not have access to any of that. (Player 2.3)A sense of autonomy or control over the work was important to participants, particularly in response to the uncertainty and lack of routine reported above as a demand. Autonomy is also an important resource that can help reduce experiences of burnout . InterviThe first time when I got a session to go shoot [on my own] in Cairns, outside of practice and stuff, I was shooting up, put my headphones on and I just started crying as I was shooting, but it was a good cry. It was like… It was a release that I had just been holding onto for so long. (Player 3.2)Good working relationships, with teammates, family and friends, mentors, and between teams, were a significant resource for all interview participants to counteract the demands of the hub. Participants spent the most amount of time with their teammates, who were going through the same demands and experiences. This shared struggle throughout the duration of the hub brought the interview participants closer to their teammates and in some teams helped them to form lifelong bonds, as described by Player Nine who only participated in one interview in the final week of the season:These are girls that I think I’m going to be friends with forever now. So, it’s been so good. The things we’ve been doing together, outside of basketball, like how I said we were going to Magnetic Island, none of that has been a forced team bonding thing. We all want to go hang out together all the time. (Player 9.1)As well as friendships within the team, informal mentoring reportedly took place to help less experienced players navigate the challenges of playing elite sport, and the new hub environment. Informal mentoring was easier to access in the 2020 WNBL season compared to previous seasons because, due to their constant proximity, younger players were able to see every part of the more experienced players’ routine and learn how they operate when under pressure, developing their skills on and off the basketball court.Participants who had strong family or external support also considered relationships external to basketball as a resource. Even though they were isolated in a hub, having family or friends away from basketball that they could talk to on the phone, or come to Queensland and watch the games, was helpful in dealing with the cognitive demand of limited time away from basketball:A couple of the girls have had their partners come out, which has been great. And then one of our team members is actually, she’s from Cairns, so she had a lot of her family at the games. And then yeah, a couple of other girls their husbands are coming up in the next couple of days. So that’ll be really good for them. A couple of players, it’s nice to have supporters in the crowd and they’ll probably be super loud. But yeah, I think it will just be really good for them. They’ll probably be a lot happier with their husbands up here. (Player 7.2)Finally, relationships between players on different teams were also a key resource. Unlike past seasons, teams were living and working at the same hotel and basketball courts for extended periods of time. Particularly for older or more experienced participants who had played on other teams, this meant they had an opportunity to spend time with friends who were not on their team, but still understood the mutual challenges of the hub, a unique resource in the 2020 season.This research explores the experience of elite women athletes living and working in a COVID-19 safe hub in Queensland, Australia for the duration of the 2020 WNBL season. The research contributes to the understanding of the management of elite sport competitions in a hub environment during a pandemic by exploring how the hub impacted on athlete wellbeing and performance. The study also extends Bakker and Demerouti’s JD-R model (2014) into a new and unusual context. The application of the JD-R model in this qualitative study has used the athlete’s voice to understand the effects of the hub on athletes over time and enabled the emergence of key themes that participants found challenging or demanding in the hub, as well as the supports or resources that were provided to athletes across multiple organizations (or different clubs) to counter those demands. It was particularly useful to develop an understanding that in the WNBL hub the resources were not sufficient to counter the demands for athletes, resulting in a higher risk of burnout, characterized by reduced motivation as well as physical and emotional exhaustion among participants. This finding adds to van Woerkom et al.’s  researchThe hub was an intense and condensed version of a normal WNBL season, and as such existing demands were more obvious or exacerbated for athletes and emerged more clearly in the data. Overload was heightened in the hub compared to previous seasons because the games were closer together and teams had to fit training and other work or study around them, which athletes discussed leading to both physical and emotional fatigue that compounded over the course of the hub. Physical recovery services were provided to deal with overload, including recovery centers, ice baths, and physiotherapy services, but they were inequitably distributed or not sufficient for athlete needs, and did not address cognitive or emotional demands. The inequity of resource provision was in part due to the different ownership structures of the clubs involved in the WNBL, and the lack of media exposure and funding that women sports are given, which also exists in a regular WNBL season , 37. ThiAs well as living in close proximity to other teams, athletes were living with and constantly surrounded by their own teammates and trying to fit in basketball-related work and activities. Therefore, there was less downtime available for athletes to stop thinking about basketball or work on something other than basketball, in order to ‘detach’ . This laFurthermore, while uncertainty was inherent in the changing landscape of a global pandemic, more could have been done to ask participants how much time and court space they needed for their pre- and post-game routines. This consultation could then have been used to give more control to athletes in building a more consistent and appropriate schedule (weekly and daily), as well as increased court time for low intensity shoot arounds in order to combat both overload and underload, acknowledging that with limited courts this will always be a challenge. Taking athlete perspectives into account in this way, and giving them greater control and agency over their work, is also understood to be an essential cognitive resource which can reduce burnout risk .Finally, it was clear that towards the end of the season, compared to the first two weeks, participants were physically and emotionally exhausted. This resulted in reduced motivation and feelings of wanting to go home rather than continue competing. Elite sport operates in a complex organizational environment and is an inherently stressful enterprise . As demoThe use of the JD-R model in a qualitative study was successful in determining that there was a gap between resources provided to participants and the demands unique to the WNBL hub model, in addition to the demands that already existed for athletes in the WNBL. To the best of our knowledge these demands, the efficacy of the resources provided to respond to the demands, and athletes’ experiences of them, have not before been reported in the literature at the time of writing. The intensity of the hub model for athletes also likely magnified issues present in all WNBL seasons. Therefore, by understanding the experience of the athletes in this new and unusual setting, this study allows us to understand the impact of the hub on athlete wellbeing and performance in both hub and other WNBL season models. These results are helpful for the management of elite sport leagues and teams, in particular when considering new settings where athletes’ routines are disrupted, and they are taken away from their home base and support networks for extended periods of time.S1 TableMain concepts and example questions for each interview.(DOCX)Click here for additional data file.S2 TableNodes and examples of sub-nodes that were used to code the qualitative data.(DOCX)Click here for additional data file. 22 Apr 2022 <div> PONE-D-21-40132 </div>  <div> Elite sport hubs during COVID-19: The job demands and resources that exist for athletes </div>  <div> PLOS ONEDear Dr. Marshall,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. 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Reviewer #1: The aim of the study was to qualitatively investigate the impacts of a hub model on athletes over time, and what job demands and resources existed for athletes based on the Job Demands-Resources Model. Overall this is a very interesting, well presented manuscript. It address a contemporary issue in elite sport, and more importantly, focuses on the lived experience of female athletes - an area seldom explored. The following comments are made in an effort to help the author further improve the quality of their manuscript.Introduction is well written, and provides a complete background of relevant literature to setup for the results and discussion.Page 4 Line 68 - suggest inclusion of a reference herePage 5 Line 94 - its not immediately clear what is/ are "burnout pathways" - suggest to provide some explanation of what this is, or alternatively improve clarity around terminology here Page 7 Line 146 Is there a reason the DISC model was not used in your study? Particularly as it was stated that it has been used in both elite and semi elite athletesSuggest to provide some further clarity around methodology.Page 9 Line 185 Was the maximum allotted time you had (30min) sufficient for data to reach a  saturation point?Similar to the above query, did any new themes continue to emerge?Page 8 Line 192 Suggest to consider providing some exemplar questions used in the interviewing to improve transparencyResults section is well laid out and use of subheadings clearly guide the readerPage 23 Line534-534 Suggest this sentence needs to be more clearly linked to data/ results sectionReviewer #2: Thanks for the opportunity to read such an interesting paper.The only revision am suggesting realties to the lit review. there are many NEW papers that can be added, and most importantly, to discuss in relation to the paper's finds. Those include:Samuel, R. D., Tenenbaum, G., & Galily, Y. (2020). The 2020 coronavirus pandemic as a change-event in sport performers’ careers: conceptual and applied practice considerations. Frontiers in Psychology, 2522.Pons, J., Ramis, Y., Alcaraz, S., Jordana, A., Borrueco, M., & Torregrossa, M. (2020). Where did all the sport go? negative impact of COVID-19 lockdown on life-spheres and mental health of spanish young athletes. Frontiers in Psychology, 11, 3498.Fiorilli, G., Grazioli, E., Buonsenso, A., Di Martino, G., Despina, T., Calcagno, G., & Di Cagno, A. (2021). A national COVID-19 quarantine survey and its impact on the Italian sports community: Implications and recommendations. Plos one, 16(3), e0248345.Washif, J. A., Farooq, A., Krug, I., Pyne, D. B., Verhagen, E., Taylor, L., ... & Chamari, K. (2022). Training during the COVID-19 lockdown: Knowledge, beliefs, and practices of 12,526 athletes from 142 countries and six continents. Sports Medicine, 52(4), 933-948.**********  <font color="black"> 6. 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Please note that Supporting Information files do not need this step. </div> While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  25 May 2022Thank you for these helpful comments and suggestions to improve our work. Similar to the Response to Reviewers, please see below each comment, and the action taken in response to that comment. 1. Page 4 Line 68 - suggest inclusion of a reference here Page 4 Line 75 now includes the reference: Bowes et al., 2020 (13) has been included. Text now reads: ‘…compared to their male counterparts (13).’2. Page 5 Line 94 - its not immediately clear what is/ are "burnout pathways" - suggest to provide some explanation of what this is, or alternatively improve clarity around terminology here  Page 5 Line 101 has been changed to: ‘…the literature around the ways in which burnout develops in athletes is minimal, but that some athletes may experience burnout as a result of …’3. Page 7 Line 146 Is there a reason the DISC model was not used in your study? Particularly as it was stated that it has been used in both elite and semi elite athletes Whilst the DISC model, which focuses on ‘matching’ job demands with job resources to mitigate the demands, has been used in the sport context previously, in this study the authors made the choice to use the original JDR model instead. The goal of this preliminary study was to gain a broad understanding of the demands and resources that WNBL athletes experienced in the hub, not to match them. Thus, the DISC model was not appropriate as we did not seek to match demands and resources. 4. Suggest to provide some further clarity around methodology.a. Page 9 Line 185 Was the maximum allotted time you had (30min) sufficient for data to reach a  saturation point?b. Similar to the above query, did any new themes continue to emerge?c. Page 8 Line 192 Suggest to consider providing some exemplar questions used in the interviewing to improve transparency Changed to, on Page 10, Line 192: “On average, each interview was 30 minutes duration, which, with the multiple sequential method and considering the limited time athletes had available away from their basketball duties and recovery time, allowed data saturation to be reached and no new themes emerged in the third interviews with participants.”The full interview guide is provided in the appendices, Page 31, line 700, to maximize transparency of our methods. 5. Page 23 Line534-534 Suggest this sentence needs to be more clearly linked to data/ results section.Changed to, at Page 24, Line 540: Taking athlete perspectives into account in this way, and giving them greater control and agency over their work, is also understood to be an essential cognitive resource…”Athlete control and agency is a key section of the results. 6. Samuel, R. D., Tenenbaum, G., & Galily, Y. (2020). The 2020 coronavirus pandemic as a change-event in sport performers’ careers: conceptual and applied practice considerations. Frontiers in Psychology, 2522. Added into Page 3, Line 40: Indeed, the pandemic has been conceptualized as a ‘longitudinal, multifaceted, unpredicted, non-controlled change event’ which impacted athletic careers and had varying impacts on the mental health of athletes .7. Pons, J., Ramis, Y., Alcaraz, S., Jordana, A., Borrueco, M., & Torregrossa, M. (2020). Where did all the sport go? negative impact of COVID-19 lockdown on life-spheres and mental health of spanish young athletes. Frontiers in Psychology, 11, 3498. Added into Page 3, Line 40: Indeed, the pandemic has been conceptualized as a ‘longitudinal, multifaceted, unpredicted, non-controlled change event’ which impacted athletic careers and had varying impacts on the mental health of athletes . 8. Fiorilli, G., Grazioli, E., Buonsenso, A., Di Martino, G., Despina, T., Calcagno, G., & Di Cagno, A. (2021). A national COVID-19 quarantine survey and its impact on the Italian sports community: Implications and recommendations. Plos one, 16(3), e0248345. Added at Page 3, Line 63: Psychologically the pandemic increased demands on athletes, both elite and amateur, and women athletes were found to be more stressed and distressed than male athletes (14). 9. Washif, J. A., Farooq, A., Krug, I., Pyne, D. B., Verhagen, E., Taylor, L., ... & Chamari, K. (2022). Training during the COVID-19 lockdown: Knowledge, beliefs, and practices of 12,526 athletes from 142 countries and six continents. Sports Medicine, 52(4), 933-948. Added in at Page 3, Line 54: The Covid-19 pandemic impacted athletes’ training and competition, affecting their fitness and skill maintenance and development, as well as their mental health (10). 30 May 2022Elite sport hubs during COVID-19: The job demands and resources that exist for athletesPONE-D-21-40132R1Dear Dr. Marshall,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. 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For more information, please contact Kind regards,Andrew Philip Lavender, PhDAcademic EditorPLOS ONE 27 Jun 2022PONE-D-21-40132R1 Elite sport hubs during COVID-19: The job demands and resources that exist for athletes  Dear Dr. Marshall:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Andrew Philip Lavender Academic EditorPLOS ONE


We review some semantical aspects of probability bounds from Boole’s “conditions on possible experience” violated by quantum mechanics. We also speculate about emerging space-time categories as an epiphenomenon of quantization and the resulting breakdown of relativity theory by non-unitary and non-linear processes. Suppose further Bob who is far away from Alice , also registers a random outcome. Yet, through some kind of `quantum magic’, Bob’s and Alice’s outcomes turn out to be the exact opposite of one another”. Such claims add to the quantum hocus-pocus that is staged for a lay audience willing to believe that, finally, at the end of a long road of rationality, physics has recovered magic, mysticism, and esoterics.Many presentations of what is often referred to as “Einstein’s spooky action at a distance” do not grasp the strangeness of the quantum phenomena. You hear statements such as Indeed, such relational phenomena as described in the previous paragraph, can also be realized by entirely classical means. All that is necessary is that both parties—Alice and Bob—use (hidden) shares that encode opposite directions. This can be conducted even for all conceivable directions Alice and Bob might measure. For instance, Peres introduced the following “bomb” example : consideA and B is  defined as the probability A and B are  defined to be independent if the occurrence of A has no influence on the probability of B, and vice versa; that is, A “here” could well alter or even completely determine the occurrence of B “there”, regardless of the (space-time) distance between A and B.To fully appreciate the quantum strangeness as compared to classical models such as Peres’ bomb scenario, recall that the joint probability of two events A—say, Alice’s outcome—is in no way altered by any particular outcome B on Bob’s side, and vice versa. In the aforementioned bomb example, this is guaranteed by the absence of communication or connection altering the causal  bound ,17,18,19So, what does a violation of the product rule indicate? One straightforward answer might challenge independence: it might mean that measurement on one side changes the probabilities on the other side. This comes close to Einstein’s motivations for writing the EPR paper ,21,22: tLet us recapitulate some (more) possibilities why and how independence, and thus the classical bounds on probability, Boole’s conditions of possible experience, can be violated. (In no way do I claim the following list to be exhaustive.)(i)There may be non-local communication between Alice and Bob, or between their shares, resulting in some form of contextuality. This can be achieved by either communicating the outcome of Bob’s measurement to Alice (or vice versa) ,23, or b(ii)Due to complementarity it may be improper to assume the simultaneous co-existence of the observables involved. This is corroborated by (extended) Kochen-Specker type theorems ,28,29,30(iii)Probability theory has to be modified by, for instance, either restricting sets of observables , or invoMy opinion is that there is not the slightest indication that some form of non-local communication happens after the entangled constituents are separated. Moreover, there is not the slightest evidence that the constituents carry hidden variables or hidden shares. So one should take seriously the possibility that those outcomes yielding non-classical correlations are formed (“during” or) through measurements. Nevertheless, the relational encoding of the entangled quantum state enforces a kind of non-locality (through outcome but not parameter dependence) that is “weak” in the sense that it obeys relativity theory—a peaceful coexistence of sorts between quantum and relativity ,34,35—inLet us explore the strangeness of the EPR phenomenology by concentrating on the role randomness plays in it. But first, let us recall that the randomness in classical relational encoding (such as Peres’ bomb example ) residesQuantum mechanics suggests that there are no value definite shares encoded in the constituents of entangled states. Indeed, because of the unitary state evolution , there is a tradeoff—a zero-sum game of sorts—between the relational encoding and the value definiteness of individual constituents. More explicitly, nothing in, say, the four vectors of the Bell basis  minimum : a 50:50 minimum —stating So one might say that, in contradistinction to quantum value indefiniteness, classical randomness is epistemic. The shares are value definite but these definite values are unknown. As the state is chosen (at its preparation) from a continuum of possible states, it contains an infinity of bits of information (with probability one). That is, classical states contain “very much” information. So it might be tempting to state that the classical states appear “over-defined” by requiring the existence of “too many” observables. In contradistinction, the individual properties of a quantum entangled state appears value indefinite. Such a “lean” state may contain no information about the properties of the respective individual quantum constituents whatsoever; that is, it appears “und(erd)efined”.“creatio continua”—the continuous creation of random bits that semantically are without any meaning or message or cause—on a massive scale. In more secular terms, these random bits “come or emerge from nowhere” and are consistent with the assumption of gaps in the physical description Let me dwell for one paragraph into the metaphysical status of quantum value indefiniteness [which also applies to (extended) Kochen-Specker theorems]. We might be tempted to ask: if it is not epistemic randomness residing in the value definiteness of the shares, exactly where does the randomness originate? The standard canonical answer might be: as the individual states of the fragments or shares are value indefinite, measurement of such non-existing “properties” must result in irreducible indeterminism . In theocription ,3, § IIIcription , as expoOne might try to conceptualize measurements by modeling them by some sort of amplifiers of micro-physical signals (quantum states) to the macroscopic level, as proposed by Glauber’s treatment of cat states . In suchSo if the measurement outcome is entirely due to noise on either side , and both sides are  separated—how come there is a relational encoding? This question is particularly pressing in the case of total alignment of Alice’s and Bob’s instruments on either side.One might attempt to answer: if one would merely consider the constituents of an entangled pair on the  separated ends of the EPR setup, one could not understand relational encoding because one would improperly interpret or view the quantum share—the entangled state—from a (separate) observable context. A proper context needs to view the share in its full context, which requires the inclusion of all (in this case two) constituents involved. This is the “message” of inseparable, entangled states of two or more constituent quanta.A caveat is in order: the following section is very speculative and should be perceived accordingly.In Glauber’s model  mentioneFrom the relational encoding, the “peaceful coexistence” appears straightforward: because for relational encoding, the only information is about the relative states of its constituents, not about the individual constituents. Therefore, no communication of any sort can be performed by variation of the properties of the individual constituents.(i)When can two outcomes be considered independent and separated? I guess one could attempt to interpret “spatial separation”—two distinct points in space-time—by decomposability of the quantum state; that is, whether the states of the constituents factorize.(ii)Can it occur that, for two (or more) of the same constituents, some of their observables factorize  and are therefore categorized as “spatially separate or apart”, and other observables are inseparable ? This results in a notion of “spatial separateness or apartness” that means relative with respect to the observables continued. Consequently, there is no absolute notion of spatial separation unless all observables are disentangled.(iii)Can it happen that all observables of two quanta are disentangled? My best guess is: for-all-practical-purposes (FAPP ) yes, bu(iv)Is it possible to generate emerging space-time categories such as frames or coordinatizations, by purely quantum mechanical means?This represents a very radical digression from relativity theory; in particular for spatial separability. Some immediate questions arise:Again, a caveat is in order: the following section is very speculative and conjectural.Because of the linear, unitary quantum evolution —essentially restricting quantum evolution to one-to-one processes—the no-cloning theorem  is a stands for the probability amplitude of absorption of a single photon when no other photon  is present [For the sake of examples, I have put forward a scheme based on the stimulated emission of photons in the presence (or absence) of “a large number” of photons in identical states . The Bos present , § 4.4. … we can fall into all kinds of traps” by rectifying—“to take too realistically—concepts such as wave and particle” [One might argue against such scenarios by stating that such statistical processes can occur only at the site of the production of particles; in this case, for instance, inside the non-linear crystal emitting two photons in a singlet state. However, delayed-choice experiments and the aforementioned non-local aspects of entanglement might put such counter-arguments in question. Of course one has to be cautious and remember that, “when analyzing article” .We have reviewed several (mis)conceptions regarding the EPR framework. One point made is that relationality, that is, (perfect) correlations among the constituents of once interacting particles, or partitions of objects that were once inseparable, is neither mindboggling nor “spooky”. Rather the quantum “advantage” or distinction over classical systems lies in the particular modulation of the respective correlations. This comes about by the particular probabilities of quantum mechanics, which need to be based on Hilbert space entities such as vectors “viewed from” orthonormal basis frames or contexts . The quaThis view also entails a modification of the space-time concept—in particular, the adjustment of an a priori Kantian framework of space-time—very much in the spirit of Einstein’s operationalization of synchronicity and space-time frames . What isWe have also speculated that perhaps not the last word is spoken about the current no-cloning concepts. While these are unavoidable consequences of, and undoubtedly valid relative to, their premises and the means involved, in particular, linearity and one-to-one unitary state evolution that essentially amounts to a “continuous permutation” of the quantum state, any digression or allowance of, say, non-linear means, could give rise to an end of Shimony’s “peaceful co-existence” ,35 of qu