Reclaiming behaviour settings: reviewing empirical applications of Barker’s behaviour settings theory
Bibliographic record
Abstract
Behaviour settings theory is the product of Roger Barker and Herbert F. Wright's decades-long Midwest Field Station research programme. The theory followed from the demonstration that the best predictor of a person's behaviour was the setting (i.e. location, timing and activity) in which their behaviour took place, rather than any individual trait (e.g. personality). Now little known in psychology, behaviour settings theory is often further obscured by being presented as a theory only, neglecting the clear methodology Barker provided for investigating the question: 'What do people do in everyday life?' This literature review takes a comprehensive look at Barker's contributions both within and outside of psychology. The corpus comprises both theoretical and empirical articles; however, our primary interest is in the empirical articles. We describe the who, when and where of behaviour settings research over the past half-century, and we identify branches and neighbours of behaviour settings research (e.g. manning theory, behaviour mapping and activity settings theory). Primarily, however, we attempt to answer the following questions: (i) Are any of Barker's tools for studying people in everyday settings being used currently? (ii) How accurately has Barker's theory been explained, or his methods applied? (iii) Does such work contribute to behaviour settings theory in a meaningful way? This article is part of the theme issue 'People, places, things and communities: expanding behaviour settings theory in the twenty-first century'.
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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 | 0.040 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.022 | 0.030 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| 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".