What If Industrial–Organizational Psychology Decided to Take Workplace Decisions Seriously?
Bibliographic record
Abstract
The major premise of this article is that increased exposure to—and increased application of—theories, methods, and findings from the judgment and decision-making (JDM) field will aid industrial–organizational psychology and organizational behavior (IOOB) researchers and practitioners in studying workplace decisions. To this end, we first provide evidence of the lack of cross-fertilization between JDM and IOOB and then provide an overview of the JDM research literature. Next, with the aid of a panel of prominent IOOB scholars who share JDM interests, we discuss the philosophical and methodological traditions in IOOB and JDM, the areas in which IOOB has already been enriched by JDM as well as the areas in which it might be further enriched in the future, ways of increasing cross-fertilization from JDM to IOOB, and ways in which IOOB can in turn contribute to JDM. Through this focal article, we hope to spark conversation and ultimately engender more cross-fertilization between JDM and IOOB.
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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 | 0.034 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".