Professional opportunities, gender obstacles, and narrowed progression: The case of the first Social Science Research Council female fellows (1925-1934).

Between the years 1925 and 1934, the Social Science Research Council (SSRC) awarded 198 postdoctoral research fellowships to early-career social scientists, among which 29 were awarded to women. This article, which is based on the SSRC directory and Rockefeller institutions' records, examines the professional paths of these female fellows to shed light on the presence of women in the social sciences and to probe the peculiarities of their professional trajectories. The SSRC fellowships represented a significant professional prospect for brilliant young female graduates who were often denied similar opportunities in other fields. Nonetheless, they did not eradicate all gender discrimination that remained prevalent, not only in the vertical sense by preventing women from progressing in the academic hierarchy, but also in the horizontal sense by retaining them in designated spaces (specific disciplines or institutions) that were underrecognized. Ultimately, the analysis of women's professional paths underscores the importance of examining the private or intimate lives of scientists to gain a more in-depth understanding of the social structure of science and its impact on its protagonists.

A dataset on survey designs and quality of social and behavioral science surveys during the COVID-19 pandemic.

In the social and behavioral sciences, surveys are frequently used to collect data. During the COVID-19 pandemic, surveys provided political actors and public health professionals with timely insights on the attitudes and behaviors of the general population. These insights were key in guiding actions to fight the pandemic. However, the data quality of these surveys remains unclear because systematic knowledge about how the survey data were collected during the COVID-19 pandemic is lacking. This is unfortunate, since decades of survey research have shown that survey design impacts data. Our Survey Data Collection and the COVID-19 Pandemic (SDCCP) project deals with this research gap. We collected rich metadata on survey design for 717 social and behavioral science surveys carried out in Germany during the first two years of the COVID-19 pandemic. In this data descriptor, we present a unique resource for a systematic assessment of the survey data collection practices and quality of surveys conducted in Germany during the COVID-19 pandemic.

La medicina social y las ciencias sociales en América Latina: tensiones conceptuales para la transformación de la salud pública en el siglo XX

[RESUMEN]. El desarrollo de la salud pública en América Latina durante el siglo XX combinó, desde el principio, el marco de la medicina social sobre los orígenes sociales, políticos y ambientales de la enfermedad con los aportes del trabajo de campo de la antropología médica. A pesar de la hegemonía del modelo médico, el surgimiento del marco de la medicina preventiva legitimó aún más la participación de los científicos sociales en el estudio de la multicausalidad de la enfermedad. Sin embargo, las limitaciones que trajo consigo la falta de contextualización histórica y política del modelo de la medicina preventiva dieron paso al movimiento latinoamericano de medicina social, basado en el materialismo histórico, y al desarrollo tanto de la epidemiología crítica como de la antropología médica crítica.

[ABSTRACT]. The development of public health in Latin America during the 20th century combined, early on, the social medicine framework on the social, political, and environmental origins of disease with the contributions of medical anthropological fieldwork. Despite the hegemony of the medical model, the surge of the preventive medicine framework further legitimized the involvement of social scientists in the study of the multicausality of disease. However, the limitations brought by the preventive medicine model’s lack of historical and political contextualization gave way to the Latin American social medicine movement, which was grounded in historical materialism, and the development of both critical epidemiology and critical medical anthropology.

[RESUMO]. Desde o início, a evolução da saúde pública na América Latina ao longo do século XX combinou o marco teórico da medicina social sobre as origens sociais, políticas e ambientais das doenças com as contribuições derivadas do trabalho de campo da antropologia médica. Apesar da hegemonia do modelo médico, o surgimento do modelo de medicina preventiva legitimou ainda mais a participação dos cientistas sociais no estudo da multicausalidade das doenças. Entretanto, as limitações causadas pela falta de contextualização histórica e política do modelo de medicina preventiva abriram espaço para o movimento latino-americano de medicina social, fundamentado no materialismo histórico, e para o desenvolvimento da epidemiologia crítica e da antropologia médica crítica.

The Reddit cannabis subjective highness rating scale: Applying computational social science to explore psychological and environmental correlates of naturalistic cannabis use.

Social media data provide unprecedented access to discussions of active, naturalistic, and often real-time cannabis use in an era of cannabis policy liberalization. The aim of this study was to explore psychological and environmental correlates of cannabis effects by applying computational social science approaches to a large dataset of unprompted reports of naturalistic cannabis use with corresponding self-reported numerical ratings of subjective highness. Post title text was extracted via the Pushshift dataset from N = 328,865 posts to the r/trees Reddit community, where posters self-assess and disclose how high they feel on a scale from 1 to 10 (M = 6.9, SD = 1.8). Structural topic modelling and Linguistic Inquiry and Word Count (LIWC) dictionary-based approaches were applied to identify (1) frequently discussed topics and (2) text indicative of 5 psychological processes (affective, social, cognitive, perceptual, biological), respectively, as well as to examine relationships between subjective highness and (1) topic prevalence and (2) psychological process word counts. A 40-topic model was selected for interpretation based on semantic coherence and exclusivity. The most discussed topics in a 40-topic model were characterized by references to smoking places, social contexts, positive affect, cognitive states, as well as food and media consumed. In LIWC dictionary analyses, words mentioning affective, social, and cognitive processes were referenced more often than perceptual or body processes. Posters reported greater subjective highness when using language that referred to in-person social environments and lower subjective highness when using language that referred to online social environments and positive affect psychological states. This examination of unprompted online reports of naturalistic cannabis use identified textual content referring to affect and to other people as being associated with perceived effects of cannabis. These affective and social aspects of the cannabis use experience were salient to active posters in this online community and should be integrated into experience sampling methods and behavioral pharmacology research, as well as public health messaging.

Can Generative AI improve social science?

Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet it is not yet known how such tools might influence social science research. I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques commonly used to study human behavior. In the second section of this article, I discuss the many limitations of Generative. I examine how bias in the data used to train these tools can negatively impact social science research-as well as a range of other challenges related to ethics, replication, environmental impact, and the proliferation of low-quality research. I conclude by arguing that social scientists can address many of these limitations by creating open-source infrastructure for research on human behavior. Such infrastructure is not only necessary to ensure broad access to high-quality research tools, I argue, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.

Re-use of research data in the social sciences. Use and users of digital data archive.

The aim of this paper is to investigate the re-use of research data deposited in digital data archive in the social sciences. The study examines the quantity, type, and purpose of data downloads by analyzing enriched user log data collected from Swiss data archive. The findings show that quantitative datasets are downloaded increasingly from the digital archive and that downloads focus heavily on a small share of the datasets. The most frequently downloaded datasets are survey datasets collected by research organizations offering possibilities for longitudinal studies. Users typically download only one dataset, but a group of heavy downloaders form a remarkable share of all downloads. The main user group downloading data from the archive are students who use the data in their studies. Furthermore, datasets downloaded for research purposes often, but not always, serve to be used in scholarly publications. Enriched log data from data archives offer an interesting macro level perspective on the use and users of the services and help understanding the increasing role of repositories in the social sciences. The study provides insights into the potential of collecting and using log data for studying and evaluating data archive use.

Where exactly do the social and behavioural sciences fit in One Health?

At its core, One Health promotes multidisciplinary cooperation amongst researchers and practitioners to improve the effectiveness and management of complex problems raised by the interplay of human, animal and environment interactions. Contemporary One Health literature has identified reducing disciplinary barriers as key to progress in the field, along with addressing the notable absence of social sciences from One Health frameworks, among other priorities. Efforts to position social scientists as experts on behaviour change and health decision-making has helped to articulate a concrete role for progressing One Health collaborations. Yet, there are other equally valuable functions the social scientist has in understanding complex systems, like One Health. We make explicit the multiple and diverse knowledge contributions the social sciences and humanities can make to progressing the One Health agenda. Articulating these more clearly invites a broader set of interdisciplinary perspectives to One Health discussions, allowing for stronger connections between sectors, actors, disciplines, and sub-systems. This perspective piece identifies a range of entry points for researchers and practitioners to better utilize the potential contributions social sciences and humanities scholars can make to One Health goals.

Evaluating Explanations From AI Algorithms for Clinical Decision-Making: A Social Science-Based Approach.

Explainable Artificial Intelligence (XAI) techniques generate explanations for predictions from AI models. These explanations can be evaluated for (i) faithfulness to the prediction, i.e., its correctness about the reasons for prediction, and (ii) usefulness to the user. While there are metrics to evaluate faithfulness, to our knowledge, there are no automated metrics to evaluate the usefulness of explanations in the clinical context. Our objective is to develop a new metric to evaluate usefulness of AI explanations to clinicians. Usefulness evaluation needs to consider both (a) how humans generally process explanations and (b) clinicians' specific requirements from explanations presented by clinical decision support systems (CDSS). Our new scoring method can evaluate the usefulness of explanations generated by any XAI method that provides importance values for the input features of the prediction model. Our method draws on theories from social science to gauge usefulness, and uses literature-derived biomedical knowledge graphs to quantify support for the explanations from clinical literature. We evaluate our method in a case study on predicting onset of sepsis in intensive care units. Our analysis shows that the scores obtained using our method corroborate with independent evidence from clinical literature and have the required qualities expected from such a metric. Thus, our method can be used to evaluate and select useful explanations from a diverse set of XAI techniques in clinical contexts, making it a fundamental tool for future research in the design of AI-driven CDSS.

Population decline: where demography, social science, and biology intersect.

In brief: Over the past half century, the world has witnessed an unprecedented decline in human fertility rates. This analysis reviews the various socioeconomic, cultural, and biological factors involved in driving this change and considers whether low fertility rates are a temporary or permanent feature of our future demographic profile. Abstract: Since the early 1960s, the world has witnessed the spectacular collapse of human fertility. As a result of this phenomenon, several countries are already seeing their population numbers fall and more will follow in the coming decades. The causes of this fertility decline involve a complex interplay of socio-economic, environmental, and biological factors that have converged to constrain fertility in posterity's wake. Since large numbers of offspring are no longer needed to compensate for high infant mortality in contemporary society, couples have opted to have small families in a quality-over-quantity investment in their progeny's future. Simultaneously, increases in female education, the enhanced participation of women in the paid workforce, and a resultant delay in childbearing has placed limits on achievable family size. Progressive urbanization, the improved availability of contraceptives, and the socio-economic pressures experienced by young adults in ageing societies are also contributing to fertility's demise. These factors, together with the individualism that pervades modern society and the increasing social acceptability of voluntary childlessness, have firmly established a low fertility ethos in most post-transition countries. Since none of these forces are about to relent, it looks as if extremely low fertility might be with us for some time to come. This may have long-term consequences. The lack of selection pressure on high fertility genotypes, the ability of ART to retain poor fertility genotypes within the population, and sustained exposure to reproductive toxicants in modern industrialized environments may all contrive to leave a permanent mark on the fecundity of our species.

Multidisciplinary teams and social science: a patient perspective.

Multidisciplinary teams (MDTs) are common in colorectal cancer and have been deemed important when providing care. Yet they take place outside of the patient, often with little consideration of the patient's views, goals and desires. In this paper specific examples from a patient perspective are integrated with the social science literature to provide an overview of areas of disconnect between MDT recommendations and the individual patient. The reasons for these disconnects are explored, including how MDTs relate to dyadic patient-clinician relationships, weak incorporation of patient-oriented outcomes in MDTs, poor integration of nonmedical patient information and the patient perspective and the impact of team dynamics and cognitive decision biases. Consideration of these issues should facilitate higher-quality MDT recommendations that are also more acceptable to patients.

Exposing omitted moderators: Explaining why effect sizes differ in the social sciences.

Policymakers increasingly rely on behavioral science in response to global challenges, such as climate change or global health crises. But applications of behavioral science face an important problem: Interventions often exert substantially different effects across contexts and individuals. We examine this heterogeneity for different paradigms that underlie many behavioral interventions. We study the paradigms in a series of five preregistered studies across one in-person and 10 online panels, with over 11,000 respondents in total. We find substantial heterogeneity across settings and paradigms, apply techniques for modeling the heterogeneity, and introduce a framework that measures typically omitted moderators. The framework's factors (Fluid Intelligence, Attentiveness, Crystallized Intelligence, and Experience) affect the effectiveness of many text-based interventions, producing different observed effect sizes and explaining variations across samples. Moderators are associated with effect sizes through two paths, with the intensity of the manipulation and with the effect of the manipulation directly. Our results motivate observing these moderators and provide a theoretical and empirical framework for understanding and predicting varying effect sizes in the social sciences.

The long-term expansion and recession of human populations.

Over the last 12,000 y, human populations have expanded and transformed critical earth systems. Yet, a key unresolved question in the environmental and social sciences remains: Why did human populations grow and, sometimes, decline in the first place? Our research builds on 20 y of archaeological research studying the deep time dynamics of human populations to propose an explanation for the long-term growth and stability of human populations. Innovations in the productive capacity of populations fuels exponential-like growth over thousands of years; however, innovations saturate over time and, often, may leave populations vulnerable to large recessions in their well-being and population density. Empirically, we find a trade-off between changes in land use that increase the production and consumption of carbohydrates, driving repeated waves of population growth over thousands of years, and the susceptibility of populations to large recessions due to a lag in the impact of humans on resources. These results shed light on the long-term drivers of human population growth and decline.