What Does Studying Occasion Setting Mean? Commentary on Leising et al. (2025) "Are You Studying Occasion Setting? A Review for Inquiring Minds"
Bibliographic record
Abstract
The term occasion setter has been used in behavioral research to describe a procedure, a phenomenon, and an associative explanatory mechanism, referring to hierarchical stimulus representations where one stimulus modulates another's relationship with an outcome.Leising et al. highlight the conditions and behavioral effects of occasion setting while applying four key tests to explore underlying associative mechanisms.However, researchers unfamiliar with associative learning may be inadvertently confused about the appropriate use of the term.Addressing this issue, this commentary underscores the need for clarity in defining what studying occasion setting means within specific research contexts.Explicitly distinguishing between its procedural, phenomenological, and mechanistic applications will help ensure consistent interpretation and communication across studies, fostering a more precise understanding of occasion setting in behavioral science.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".