Why People Do What They Do: An Interdisciplinary Synthesis of Human Action Theories
Bibliographic record
Abstract
Understanding why people do what they do is central to advancing equitable and sustainable futures. Yet, theories about human action are fragmented across many social science disciplines, each with its own jargon and implicit assumptions. This fragmentation has hindered theory integration and accessibility of theories relevant to a given challenge. We synthesized human action theories from across the humanities and social sciences. We developed eight underlying assumptions—metatheories—that reveal a fundamental organization of human action theories. We describe each metatheory and the challenges that it best elucidates (illustrated with climate change examples). No single metatheory addresses the full range of factors and problems; only one treats interactions between factors. Our synthesis will help researchers, policymakers, and practitioners gain a multifaceted understanding of human action.
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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 | 0.038 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".