Supplementary material from "A Practitioner’s Field Guide to the Behaviour Settings Method"
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.
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.416 | 0.181 |
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".