Report of the ACBS Task Force on the strategies and tactics of contextual behavioral science research
Bibliographic record
Abstract
Throughout its history the strategy and tactics of contextual behavioral science (CBS) research have had distinctive features as compared to traditional behavioral science approaches. Continued progress in CBS research can be facilitated by greater clarity about how its strategy and tactics can be brought to bear on current challenges. The present white paper is the result of a 2 1/2-year long process designed to foster consensus among representative producers and consumers of CBS research about the best strategic pathway forward. The Task Force agreed that CBS research should be multilevel, process-based, multidimensional, prosocial, and pragmatic, and provided 33 recommendations to the CBS community arranged across these characteristics. In effect, this report provides a detailed research agenda designed to maximize the impact of CBS as a field. Scientists and practitioners are encouraged to mount this ambitious agenda.
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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 | 0.254 | 0.231 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier 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".