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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.840 | 0.003 |
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; both teacher heads agree on what is shown here.
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".