The Biosocial Approach to Human Development, Behavior, and Health Across the Life Course
Bibliographic record
Abstract
Social and biological phenomena are widely recognized as determinants of human development, health, and socioeconomic attainments across the life course, but our understanding of the underlying pathways and processes remains limited. To address this gap, we define the "biosocial approach" as one that conceptualizes the biological and social as mutually constituting, and that draws on models and methods from the biomedical and social/behavioral sciences. By bringing biology into the social sciences, we can illuminate mechanisms through which socioeconomic, psychosocial, and other contextual factors shape human development and health. Human biology is a social biology, and biological measures can therefore identify aspects of social contexts that are harmful, as well as beneficial, with respect to well-being. By bringing social science concepts and study designs to biology and biomedicine, we encourage an epistemological shift that foregrounds social/contextual factors as important determinants of human biology and health. The biosocial approach also underscores the importance of the life course, as assessments of both biological and social features throughout human development over time, and across generations, are needed to achieve a full understanding of social and physical well-being. We conclude with a brief review of the papers in the volume, which showcase the value of a biosocial approach to understanding the pathways linking social stratification, biology, and health across the life course.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".