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The US higher education system concentrates the production of science and scientists within a few institutions. This has implications for minoritized scholars and the topics with which they are disproportionately associated. This paper examines topical alignment between institutions and authors of varying intersectional identities, and the relationship with prestige and scientific impact. We observe a Howard-Harvard effect, in which the topical profile of minoritized scholars are amplified in mission-driven institutions and decreased in prestigious institutions. Results demonstrate a consistent pattern of inequality in topics and research impact. Specifically, we observe statistically significant differences between minoritized scholars and White men in citations and journal impact. The aggregate research profile of prestigious US universities is highly correlated with the research profile of White men, and highly negatively correlated with the research profile of minoritized women. Furthermore, authors affiliated with more prestigious institutions are associated with increasing inequalities in both citations and journal impact. Academic institutions and funders are called to create policies to mitigate the systemic barriers that prevent the United States from achieving a fully robust scientific ecosystem.

The rapid emergence of generative Language Models (LMs) has led to growing concern about the impacts that their unexamined adoption may have on the social well-being of diverse user groups. Meanwhile, LMs are increasingly being adopted in K-20 schools and one-on-one student settings with minimal investigation of potential harms associated with their deployment. Motivated in part by real-world/everyday use cases (e.g., an AI writing assistant) this paper explores the potential psychosocial harms of stories generated by five leading LMs in response to open-ended prompting. We extend findings of stereotyping harms analyzing a total of 150K 100-word stories related to student classroom interactions. Examining patterns in LM-generated character demographics and representational harms (i.e., erasure, subordination, and stereotyping) we highlight particularly egregious vignettes, illustrating the ways LM-generated outputs may influence the experiences of users with marginalized and minoritized identities, and emphasizing the need for a critical understanding of the psychosocial impacts of generative AI tools when deployed and utilized in diverse social contexts., Comment: Presented in Impact of GenAI on Social and Individual Well-being at AAAI 2024 Spring Symposium Series (2024)

As technology advances, conceptualizations of effective strategies for teaching and learning shift. Due in part to their facilitation of unique affordances for learning, mobile devices, augmented reality, and games are all becoming more prominent elements in learning environments. In this work, we examine mobile augmented reality serious games (MARSGs) as the intersection of these technology-based experiences and to what effect their combination can yield even greater learning outcomes. We present a PRISMA review of 23 papers (from 610) spanning the entire literature timeline from 2002–2023. Among these works, there is wide variability in the realized application of game elements and pedagogical theories underpinning the game experience. For an educational tool to be effective, it must be designed to facilitate learning while anchored by pedagogical theory. Given that most MARSG developers are not pedagogical experts, this review further provides design considerations regarding which game elements might proffer the best of three major pedagogical theories for modern learning (cognitive constructivism, social constructivism, and behaviorism) based on existing applications. We will also briefly touch on radical constructivism and the instructional elements embedded within MARSGs. Lastly, this work offers a synthesis of current MARSG findings and extended future directions for MARSG development.

One of six research themes outlined in the 2021 Strategic Delivery Plan for UK Defence Medical Services (DMS) Research 2021–2026 is ‘preventing and treating musculoskeletal injury (MSKI)’.1 The research priorities identified include: ‘injury prevention and prehabilitation’, ‘lower-limb injury’, ‘shortened time to return-to-service’ and ‘physical comorbidity’. The strategic development plan also identified a need for research investigating ‘factors affecting deployment suitability and how they can be assessed and mitigated’. . . .

Investments in research and development are key to scientific and economic growth and to the well-being of society. Scientific research demands significant resources making national scientific investment a crucial driver of scientific production. As scientific production becomes increasingly multinational, it is critical to study how nations' scientific activities are funded both domestically and internationally. By tracing research grants acknowledged in scholarly publications, our study reveals a shifting duopoly of China and the United States in the global funding landscape, with a contrasting funding pattern; while China has surpassed the United States in publications with acknowledged domestic and international funding, the United States largely maintains its role as the most important global research partner. Our results also highlight the precarity of low- and middle-income countries to global funding disruptions. By revealing the complex interdependence and collaboration between countries in the global scientific enterprise, this work informs future studies investigating the national and global scientific enterprise and how funding leads to both productive cooperation and vulnerable dependencies., Comment: 48 pages, 13 figures

Responsible indicators are crucial for research assessment and monitoring. Transparency and accuracy of indicators are required to make research assessment fair and ensure reproducibility. However, sometimes it is difficult to conduct or replicate studies based on indicators due to the lack of transparency in conceptualization and operationalization. In this paper, we review the different variants of the Probabilistic Affinity Index (PAI), considering both the conceptual and empirical underpinnings. We begin with a review of the historical development of the indicator and the different alternatives proposed. To demonstrate the utility of the indicator, we demonstrate the application of PAI to identifying preferred partners in scientific collaboration. A streamlined procedure is provided, to demonstrate the variations and appropriate calculations. We then compare the results of implementation for 5 specific countries involved in international scientific collaboration. Despite the different proposals on its calculation, we do not observe large differences between the PAI variants, particularly with respect to country size. As with any indicator, the selection of a particular variant is dependent on the research question. To facilitate appropriate use, we provide recommendations for the use of the indicator given specific contexts.

The global health system (GHS) is ill-equipped to deal with the increasing number of transnational challenges. The GHS needs reform to enhance global resilience to future risks to health. In this article we argue that the starting point for any reform must be conceptualizing and studying the GHS as a complex adaptive system (CAS) with a large and escalating number of interconnected global health actors that learn and adapt their behaviours in response to each other and changes in their environment. The GHS can be viewed as a multi-scalar, nested health system comprising all national health systems together with the global health architecture, in which behaviours are influenced by cross-scale interactions. However, current methods cannot adequately capture the dynamism or complexity of the GHS or quantify the effects of challenges or potential reform options. We provide an overview of a selection of systems thinking and complexity science methods available to researchers and highlight the numerous policy insights their application could yield. We also discuss the challenges for researchers of applying these methods and for policy makers of digesting and acting upon them. We encourage application of a CAS approach to GHS research and policy making to help bolster resilience to future risks that transcend national boundaries and system scales

Human behaviour is complex and multifaceted, and is studied by a broad range of disciplines across the social and natural sciences. To mark our 5th anniversary, we asked leading scientists in some of the key disciplines that we cover to share their vision of the future of research in their disciplines. Our contributors underscore how important it is to broaden the scope of their disciplines to increase ecological validity and diversity of representation, in order to address pressing societal challenges that range from new technologies, modes of interaction and sociopolitical upheaval to disease, poverty, hunger, inequality and climate change. Taken together, these contributions highlight how achieving progress in each discipline will require incorporating insights and methods from others, breaking down disciplinary silos.

Human behaviour is complex and multifaceted, and is studied by a broad range of disciplines across the social and natural sciences. To mark our 5th anniversary, we asked leading scientists in some of the key disciplines that we cover to share their vision of the future of research in their disciplines. Our contributors underscore how important it is to broaden the scope of their disciplines to increase ecological validity and diversity of representation, in order to address pressing societal challenges that range from new technologies, modes of interaction and sociopolitical upheaval to disease, poverty, hunger, inequality and climate change. Taken together, these contributions highlight how achieving progress in each discipline will require incorporating insights and methods from others, breaking down disciplinary silos., Menschliches Verhalten ist komplex und vielschichtig und wird von einem breiten Spektrum von Disziplinen in den Sozial- und Naturwissenschaften untersucht. Anlässlich des 5-jährigen Jubiläums von "Nature Human Behavior" wurden führende Wissenschaftler*innen aus einigen Schlüsseldisziplinen gebeten, ihre Vorstellungen von der Zukunft der Forschung in ihren Disziplinen mitzuteilen. Die Beiträge unterstreichen, wie wichtig es ist, den Bereich ihrer Disziplinen zu erweitern, um die ökologische Validität und die Vielfalt der Repräsentation zu erhöhen, um dringende gesellschaftliche Herausforderungen anzugehen, die von neuen Technologien, Interaktionsformen und soziopolitischen Umwälzungen bis hin zu Krankheiten, Armut, Hunger, Ungleichheit und Klimawandel reichen. Zusammengenommen machen diese Beiträge deutlich, dass Fortschritte in den einzelnen Disziplinen nur dann erzielt werden können, wenn Erkenntnisse und Methoden aus anderen Disziplinen einbezogen und disziplinäre Silos aufgebrochen werden.

The US scientific workforce is primarily composed of White men. Studies have demonstrated the systemic barriers preventing women and other minoritized populations from gaining entry to science; few, however, have taken an intersectional perspective and examined the consequences of these inequalities on scientific knowledge. We provide a large-scale bibliometric analysis of the relationship between intersectional identities, topics, and scientific impact. We find homophily between identities and topic, suggesting a relationship between diversity in the scientific workforce and expansion of the knowledge base. However, topic selection comes at a cost to minoritized individuals for whom we observe both between- and within-topic citation disadvantages. To enhance the robustness of science, research organizations should provide adequate resources to historically underfunded research areas while simultaneously providing access for minoritized individuals into high-prestige networks and topics.

The US scientific workforce is primarily composed of White men. Studies have demonstrated the systemic barriers preventing women and other minoritized populations from gaining entry to science; few, however, have taken an intersectional perspective and examined the consequences of these inequalities on scientific knowledge. We provide a large-scale bibliometric analysis of the relationship between intersectional identities, topics, and scientific impact. We find homophily between identities and topic, suggesting a relationship between diversity in the scientific workforce and expansion of the knowledge base. However, topic selection comes at a cost to minoritized individuals for whom we observe both between- and within-topic citation disadvantages. To enhance the robustness of science, research organizations should provide adequate resources to historically underfunded research areas while simultaneously providing access for minoritized individuals into high-prestige networks and topics.

Racial disparity in academia is a widely acknowledged problem. The quantitative understanding of racial-based systemic inequalities is an important step towards a more equitable research system. However, because of the lack of robust information on authors’ race, few large-scale analyses have been performed on this topic. Algorithmic approaches offer one solution, using known information about authors, such as their names, to infer their perceived race. As with any other algorithm, the process of racial inference can generate biases if it is not carefully considered. The goal of this article is to assess the extent to which algorithmic bias is introduced using different approaches for name-based racial inference. We use information from the U.S. Census and mortgage applications to infer the race of U.S. affiliated authors in the Web of Science. We estimate the effects of using given and family names, thresholds or continuous distributions, and imputation. Our results demonstrate that the validity of name-based inference varies by race/ethnicity and that threshold approaches underestimate Black authors and overestimate White authors. We conclude with recommendations to avoid potential biases. This article lays the foundation for more systematic and less-biased investigations into racial disparities in science.

Racial disparity in academia is a widely acknowledged problem. The quantitative understanding of racial-based systemic inequalities is an important step towards a more equitable research system. However, because of the lack of robust information on authors’ race, few large-scale analyses have been performed on this topic. Algorithmic approaches offer one solution, using known information about authors, such as their names, to infer their perceived race. As with any other algorithm, the process of racial inference can generate biases if it is not carefully considered. The goal of this article is to assess the extent to which algorithmic bias is introduced using different approaches for name-based racial inference. We use information from the U.S. Census and mortgage applications to infer the race of U.S. affiliated authors in the Web of Science. We estimate the effects of using given and family names, thresholds or continuous distributions, and imputation. Our results demonstrate that the validity of name-based inference varies by race/ethnicity and that threshold approaches underestimate Black authors and overestimate White authors. We conclude with recommendations to avoid potential biases. This article lays the foundation for more systematic and less-biased investigations into racial disparities in science.

The global health system (GHS) is ill-equipped to deal with the increasing number of transnational challenges. The GHS needs reform to enhance global resilience to future risks to health. In this article we argue that the starting point for any reform must be conceptualizing and studying the GHS as a complex adaptive system (CAS) with a large and escalating number of interconnected global health actors that learn and adapt their behaviours in response to each other and changes in their environment. The GHS can be viewed as a multi-scalar, nested health system comprising all national health systems together with the global health architecture, in which behaviours are influenced by cross-scale interactions. However, current methods cannot adequately capture the dynamism or complexity of the GHS or quantify the effects of challenges or potential reform options. We provide an overview of a selection of systems thinking and complexity science methods available to researchers and highlight the numerous policy insights their application could yield. We also discuss the challenges for researchers of applying these methods and for policy makers of digesting and acting upon them. We encourage application of a CAS approach to GHS research and policy making to help bolster resilience to future risks that transcend national boundaries and system scales.

The global health system (GHS) is ill-equipped to deal with the increasing number of transnational challenges. The GHS needs reform to enhance global resilience to future risks to health. In this article we argue that the starting point for any reform must be conceptualizing and studying the GHS as a complex adaptive system (CAS) with a large and escalating number of interconnected global health actors that learn and adapt their behaviours in response to each other and changes in their environment. The GHS can be viewed as a multi-scalar, nested health system comprising all national health systems together with the global health architecture, in which behaviours are influenced by cross-scale interactions. However, current methods cannot adequately capture the dynamism or complexity of the GHS or quantify the effects of challenges or potential reform options. We provide an overview of a selection of systems thinking and complexity science methods available to researchers and highlight the numerous policy insights their application could yield. We also discuss the challenges for researchers of applying these methods and for policy makers of digesting and acting upon them. We encourage application of a CAS approach to GHS research and policy making to help bolster resilience to future risks that transcend national boundaries and system scales.

The global health system (GHS) is ill-equipped to deal with the increasing number of transnational challenges. The GHS needs reform to enhance global resilience to future risks to health. In this article we argue that the starting point for any reform must be conceptualizing and studying the GHS as a complex adaptive system (CAS) with a large and escalating number of interconnected global health actors that learn and adapt their behaviours in response to each other and changes in their environment. The GHS can be viewed as a multi-scalar, nested health system comprising all national health systems together with the global health architecture, in which behaviours are influenced by cross-scale interactions. However, current methods cannot adequately capture the dynamism or complexity of the GHS or quantify the effects of challenges or potential reform options. We provide an overview of a selection of systems thinking and complexity science methods available to researchers and highlight the numerous policy insights their application could yield. We also discuss the challenges for researchers of applying these methods and for policy makers of digesting and acting upon them. We encourage application of a CAS approach to GHS research and policy making to help bolster resilience to future risks that transcend national boundaries and system scales.

Diversity in human capital is widely seen as critical to creating holistic and high quality research, especially in areas that engage with diverse cultures, environments, and challenges. Quantifying diverse academic collaborations and its effect on research quality is lacking, especially at international scale and across different domains. Here, we present the first effort to measure the impact of geographic diversity in coauthorships on the citation of their papers across different academic domains. Our results unequivocally show that geographic coauthor diversity improves paper citation, but very long distance collaborations has variable impact. We also discover "well-trodden" collaboration circles that yield much less impact than similar travel distances. These relationships are observed to exist across different subject areas, but with varying strengths. These findings can help academics identify new opportunities from a diversity perspective, as well as inform funders on areas that require additional mobility support., Comment: 26 pages, 16 figures

Human behaviour is complex and multifaceted, and is studied by a broad range of disciplines across the social and natural sciences. To mark our 5th anniversary, we asked leading scientists in some of the key disciplines that we cover to share their vision of the future of research in their disciplines. Our contributors underscore how important it is to broaden the scope of their disciplines to increase ecological validity and diversity of representation, in order to address pressing societal challenges that range from new technologies, modes of interaction and sociopolitical upheaval to disease, poverty, hunger, inequality and climate change. Taken together, these contributions highlight how achieving progress in each discipline will require incorporating insights and methods from others, breaking down disciplinary silos.

Human behaviour is complex and multifaceted, and is studied by a broad range of disciplines across the social and natural sciences. To mark our 5th anniversary, we asked leading scientists in some of the key disciplines that we cover to share their vision of the future of research in their disciplines. Our contributors underscore how important it is to broaden the scope of their disciplines to increase ecological validity and diversity of representation, in order to address pressing societal challenges that range from new technologies, modes of interaction and sociopolitical upheaval to disease, poverty, hunger, inequality and climate change. Taken together, these contributions highlight how achieving progress in each discipline will require incorporating insights and methods from others, breaking down disciplinary silos.

In scientometrics, it is critical to investigate the patterns of scientific collaboration and how these patterns result in different impacts. In this research, we investigate the relationship between the sequence of authors in the publication bylines and the diversity of their collaborators. The diversity of collaborators is quantified with two dimensions, namely topic and impact diversities. Using the ArnetMiner dataset containing ACM-indexed publications in computer science, we find that the following two patterns tend to lead higher-impact scientific publications: (1) greater topic diversity of collaborators plus more tendency to work as leading authors (including first and/or corresponding authors); and (2) less topic diversity of collaborators plus less tendency to work as leading authors. Meanwhile, from the perspective of impact diversity, the results of our empirical study show that authors who work as more leading authors and collaborate with less impact diversity researchers have tendencies to receive more citations than those with collaborators with greater impact diversity. We also detect different patterns of authors’ sequence and diversity of their collaborators before and after their Ph.D. graduation.

National contributions to science are influenced by a number of factors, including economic capacity, national scientific priorities, science policy, and institutional settings and cultures. Nations do not have equal opportunities to access the global scientific market, and therefore, often seek out international partners with complementary resources and expertise. This study aims at investigating national collaboration strategies, with a focus on research leadership—measured through the corresponding authorship—and its relationship with scientific impact. Results show that countries with higher R&D investments are more scientifically independent, and confirm that international collaborations positively related to citation impact. However, leadership in international collaborations inversely related to a country share of international collaboration and there is a very little relationship between citation impact and international leadership. For instance, most countries—and particularly those that have fewer resources—have a higher scientific impact when they are not leading. This suggests that, despite increasing global participation in science, most international collaborations area symmetrical, and that the research system remains structured around a few dominate nations

In science of team science, it is critical to investigate the patterns of scientific collaboration and how these patterns result in different impacts. In this research, we investigate the relationship among (a) scientific collaboration, (b) the sequence of authors in the publication bylines, and (c) the diversity of their collaborators. The diversity of collaborators is quantified with two dimensions, namely topic and impact diversities. Using the ArnetMiner dataset containing ACM-indexed publications in computer science, we find that the following two patterns tend to lead higher-impact scientific publications: (1) greater topic diversity of collaborators plus more tendency to work as leading authors (including first and/or corresponding authors); and (2) less topic diversity of collaborators plus less tendency to work as leading authors. Meanwhile, from the perspective of impact diversity, the results of our empirical study show that authors who work as more leading authors and collaborate with less impact diversity researchers have tendencies to receive more citations than those with collaborators with greater impact diversity.

Rape shield laws have played an important role in protecting complainants and jurors from some of the most pernicious and ill-founded assumptions about sexual autonomy and consent. Yet the development and application of these rules have left many thorny questions. The policy debate has now shifted from whether and how the accuser's prior sexual conduct should be admitted to prove consent or lack of credibility due to what was once termed "unchastity" (now universally condemned and rightly prohibited) to whether and how the accuser's prior sexual conduct should be admitted to support a more specific and logically relevant argument for dishonesty. That is, when prior sexual conduct itself involves dishonest behavior, the defendant is not offering the prior incident to support a general character trait for mendacity because the complainant has been sexually active; rather, the defendant is arguing that the complainant has a character trait for untruthfulness because the accuser has lied. One narrow but critical question we need to confront as evidence and rape law progress during the "Me Too" movement is whether the jury, in assessing a complainant's credibility in a rape prosecution, should be allowed to hear about prior false allegations of sexual assault made by the accuser. This article focuses on rape shield rules throughout the United States, highlighting how these evidence rules have been stretched beyond their original purpose to prevent a defendant from raising incidents in the accuser's sexual history that may be highly pertinent to a jury's determination of who they should believe. Specifically, it addresses limitations courts have placed on inquiring into prior false allegations (PFA) of sexual assault by the accuser to prove lack of credibility in the present case. The author argues that some courts in the United States have mistakenly weighed the accuser's privacy interests and the court's interests in protecting the jury from being confused or misled ahe

Aims: Marijuana is a popular drug among U.S. college students. In Sweden, the prevalence of marijuana use has been relatively low but is increasing. Brief, personalized interventions have been efficacious in reducing substance use, including marijuana, among college students in the U.S. However, prior to implementation of U.S. interventions in Sweden, it is important to compare factors associated with marijuana use among college students in the two countries. Design, Setting, and Participants: Data are from baseline assessments of two large college student intervention studies in the U.S. (N = 3,753, 39% male) and Sweden (N = 2,280, 35% male). Measures: Past 30-day prevalence and frequency of marijuana use was analyzed in regard to relevant demographic factors. The moderating role of nationality was also examined. Findings: Results support previous findings indicating marijuana use is more common in the U.S. than in Sweden. Most demographic factors were similar across the countries, except for relationship status and work status, in which associations with number of marijuana use days (but not odds of any marijuana use) were stronger for Swedish college students compared to U.S. college students. Conclusions: Based on overall similarities between the U.S. and Sweden, comparable interventions might be recommended in both countries.

Aims: Marijuana is a popular drug among U.S. college students. In Sweden, the prevalence of marijuana use has been relatively low but is increasing. Brief, personalized interventions have been efficacious in reducing substance use, including marijuana, among college students in the U.S. However, prior to implementation of U.S. interventions in Sweden, it is important to compare factors associated with marijuana use among college students in the two countries. Design, Setting, and Participants: Data are from baseline assessments of two large college student intervention studies in the U.S. (N = 3,753, 39% male) and Sweden (N = 2,280, 35% male). Measures: Past 30-day prevalence and frequency of marijuana use was analyzed in regard to relevant demographic factors. The moderating role of nationality was also examined. Findings: Results support previous findings indicating marijuana use is more common in the U.S. than in Sweden. Most demographic factors were similar across the countries, except for relationship status and work status, in which associations with number of marijuana use days (but not odds of any marijuana use) were stronger for Swedish college students compared to U.S. college students. Conclusions: Based on overall similarities between the U.S. and Sweden, comparable interventions might be recommended in both countries.

Racial disparity in academia is a widely acknowledged problem. The quantitative understanding of racial-based systemic inequalities is an important step towards a more equitable research system. However, few large-scale analyses have been performed on this topic, mostly because of the lack of robust race-disambiguation algorithms. Identifying author information does not generally include the author’s race. Therefore, an algorithm needs to be employed, using known information about authors, i.e., their names, to infer their perceived race. Nevertheless, as any other algorithm, the process of racial inference can generate biases if it is not carefully considered. When the research is focused on the understanding of racial-based inequalities, such biases undermine the objectives of the investigation and may perpetuate inequities. The goal of this article is to assess the biases introduced by the different approaches used name-based racial inference. We use information from US census and mortgage applications to infer the race of US author names in the Web of Science. We estimate the effects of using given and family names, thresholds or continuous distributions, and imputation. Our results demonstrate that the validity of name-based inference varies by race and ethnicity and that threshold approaches underestimate Black authors and overestimate White authors. We conclude with recommendations to avoid potential biases. This article fills an important research gap that will allow more systematic and unbiased studies on racial disparity in science.

Racial disparity in academia is a widely acknowledged problem. The quantitative understanding of racial-based systemic inequalities is an important step towards a more equitable research system. However, few large-scale analyses have been performed on this topic, mostly because of the lack of robust race-disambiguation algorithms. Identifying author information does not generally include the author’s race. Therefore, an algorithm needs to be employed, using known information about authors, i.e., their names, to infer their perceived race. Nevertheless, as any other algorithm, the process of racial inference can generate biases if it is not carefully considered. When the research is focused on the understanding of racial-based inequalities, such biases undermine the objectives of the investigation and may perpetuate inequities. The goal of this article is to assess the biases introduced by the different approaches used name-based racial inference. We use information from US census and mortgage applications to infer the race of US author names in the Web of Science. We estimate the effects of using given and family names, thresholds or continuous distributions, and imputation. Our results demonstrate that the validity of name-based inference varies by race and ethnicity and that threshold approaches underestimate Black authors and overestimate White authors. We conclude with recommendations to avoid potential biases. This article fills an important research gap that will allow more systematic and unbiased studies on racial disparity in science.

Disagreement is essential to scientific progress. However, the extent of disagreement in science, its evolution over time, and the fields in which it happens, remains poorly understood. Leveraging a massive collection of English-language scientific texts, we develop a cue-phrase based approach to identify instances of disagreement citations across more than four million scientific articles. Using this method, we construct an indicator of disagreement across scientific fields over the 2000-2015 period. In contrast with black-box text classification methods, our framework is transparent and easily interpretable. We reveal a disciplinary spectrum of disagreement, with higher disagreement in the social sciences and lower disagreement in physics and mathematics. However, detailed disciplinary analysis demonstrates heterogeneity across sub-fields, revealing the importance of local disciplinary cultures and epistemic characteristics of disagreement. Paper-level analysis reveals notable episodes of disagreement in science, and illustrates how methodological artifacts can confound analyses of scientific texts. These findings contribute to a broader understanding of disagreement and establish a foundation for future research to understanding key processes underlying scientific progress., Comment: 49 pages, 10 figures

Racial disparity in academia is a widely acknowledged problem. The quantitative understanding of racial based systemic inequalities is an important step towards a more equitable research system. However, because of the lack of robust information on authors' race, few large scale analyses have been performed on this topic. Algorithmic approaches offer one solution, using known information about authors, such as their names, to infer their perceived race. As with any other algorithm, the process of racial inference can generate biases if it is not carefully considered. The goal of this article is to assess the extent to which algorithmic bias is introduced using different approaches for name based racial inference. We use information from the U.S. Census and mortgage applications to infer the race of U.S. affiliated authors in the Web of Science. We estimate the effects of using given and family names, thresholds or continuous distributions, and imputation. Our results demonstrate that the validity of name based inference varies by race/ethnicity and that threshold approaches underestimate Black authors and overestimate White authors. We conclude with recommendations to avoid potential biases. This article lays the foundation for more systematic and less biased investigations into racial disparities in science.

Science is essential to innovation and economic prosperity. Although studies have shown that national scientific development is affected by geographic, historic, and economic factors, it remains unclear whether there are universal structures and trajectories of national scientific development that can inform forecasting and policymaking. Here, by examining countries' scientific 'exports'-publications that are indexed in international databases-we reveal a three-cluster structure in the relatedness network of disciplines that underpin national scientific development and the organization of global science. Tracing the evolution of national research portfolios reveals that while nations are proceeding to more diverse research profiles individually, scientific production is increasingly specialized in global science over the past decades. By uncovering the underlying structure of scientific development and connecting it with economic development, our results may offer a new perspective on the evolution of global science., Comment: 30 pages(main text), 5 figures(main text), 3 tables(main text)

This study explores the international profiles in collaboration and mobility of countries included in the so-called ‘‘travel bans’’ implemented by US President Trump as an executive order in 2017. The objective of this research is to analyze the exchange of knowledge between countries and the relative importance of specific countries in order to inform evidence-based science policy. The work serves as a proof-of-concept of the utility of asymmetry and affinity indexes for collaboration and mobility. Comparative analyses of these indicators can be useful for informing immigration policies and motivating collaboration and mobility relationships—emphasizing the importance of geographic and cultural similarities. Egocentric and relational perspectives are analyzed to provide various lenses on the importance of countries. Our analysis suggests that comparisons of collaboration and mobility from an affinity perspective can identify discrepancies between levels of collaboration and mobility. This approach can inform international immigration policies and, if extended, demonstrate potential partnerships at several levels of analysis (e.g., institutional, sectoral, state/province).

This study compares the flows of mobile researchers and the number of publications in international collaboration within the context of scientific and economic capacities. The goal is to identify the convergence or discrepancy of countries in mobility and collaboration and determine the positions and relative influence of countries in both processes. Using affiliation data from scientific publications, we analyze the distributions and networks of collaboration and mobility and their structural differences. The results show that there is a significant relationship between the flow of mobile researchers and the capacity for publishing with foreign partners in the more prolific countries, although mobility is always lower than collaboration. Size matters and scientific relationship are highly resource-dependent. Advanced and Proficient countries accumulate the highest proportion of the mobile authors and international publications with an extremely low representation of mobility in Developing and Lagging countries. In addition, the placement of countries is not always consistent in both networks, revealing distinct roles of mobility and collaboration, with particular instability for lower income countries. The more resources available in a country (both scientific and economic) the greater the likelihood of attracting foreign partners and mobilizing human capital. The policy relevance of these structural differences are described and a brief description of the limitations and future research are provided.

International collaboration in the creation of knowledge is changing the structural stratification of science, with implications for science policy. Analyses of collaboration in developing and emergent countries are of particular significance because initiatives are often the result of “research-for-aid” arrangements, generally based on North-South asymmetries. However, collaboration for mutual benefit and excellence has gained increasing acceptance, with “partner” selection becoming a strategic priority to enhance one’s own production. This article explores the capacity of BRIC and selects Latin American countries in the generation of scientific knowledge and their visibility at the global level in the field of nanoscience and nanotechnology. The goal is to determine to what extent countries benefit from the role of their collaborators to heighten research performance in terms of citation; and how collaboration could help countries to leverage their competitiveness through the design of research and development agendas. The method relies on the decomposition of leadership, as well as its consideration in view of performance indicators such as normalized citation impact, scientific excellence, and technological impact. The results suggest that the growth of international collaboration should be interpreted as a positive aspect. Furthermore, a progressive internationalization of scientific activity concerned with local needs or topics of interest is found to have the capacity to determine research agendas whose interest would extend to communities far beyond, thereby contributing to the development of science at a national level.

Research careers are typically envisioned as a single path in which a scientist starts as a member of a team working under the guidance of one or more experienced scientists and, if they are successful, ends with the individual leading their own research group and training future generations of scientists. Here we study the author contribution statements of published research papers in order to explore possible biases and disparities in career trajectories in science. We used Bayesian networks to train a prediction model based on a dataset of 70,694 publications from PLoS journals, which included 347,136 distinct authors and their associated contribution statements. This model was used to predict the contributions of 222,925 authors in 6,236,239 publications, and to apply a robust archetypal analysis to profile scientists across four career stages: junior, early-career, mid-career and late-career. All three of the archetypes we found-leader, specialized, and supporting-were encountered for early-career and mid-career researchers. Junior researchers displayed only two archetypes (specialized, and supporting), as did late-career researchers (leader and supporting). Scientists assigned to the leader and specialized archetypes tended to have longer careers than those assigned to the supporting archetype. We also observed consistent gender bias at all stages: the majority of male scientists belonged to the leader archetype, while the larger proportion of women belonged to the specialized archetype, especially for early-career and mid-career researchers., Applied Probability

Scientometrics have become an essential element in the practice and evaluation of science and research, including both the evaluation of individuals and national assessment exercises. Yet, researchers and practitioners in this field have lacked clear theories to guide their work. As early as 1981, then doctoral student Blaise Cronin published The need for a theory of citing - a call to arms for the fledgling scientometric community to produce foundational theories upon which the work of the field could be based. More than three decades later, the time has come to reach out the field again and ask how they have responded to this call. This book compiles the foundational theories that guide informetrics and scholarly communication research. It is a much needed compilation by leading scholars in the field that gathers together the theories that guide our understanding of authorship, citing, and impact.