Bibliographic record
Abstract
Since the 1950s, Roger Barker’s theory of behaviour settings has been useful for a wide number of disciplines. Few realize, however, that behaviour settings theory is also a methodology. Barker fully describes how to identify, describe and measure behaviour settings in his seminal book Ecological psychology: concepts and methods for studying the environment of human behavior (1968), and this method is further delineated in Phil Schoggen’s Behavior settings: a revision and extension of Roger G. Barker’s ecological psychology (1989). Nevertheless, beyond these two (rather expensive) books there are few other resources available to twenty-first century researchers who wish to systematically describe and measure behaviour in its ecological context using the principles of behaviour settings theory. In this article, I offer a practitioner’s field guide to implementing the behaviour settings method, which includes a contemporary illustration of defining a behaviour setting using a recent observational study of an art gallery in Lethbridge, Canada. I discuss how researchers can use Barker’s original methodology to determine what is a behaviour setting and how to define its boundaries, and I suggest best practices, offering practitioners the tools to replicate Barker’s procedures. 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 | 0.078 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.038 | 0.033 |
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".