Bibliographic record
Abstract
font une analyse critique de l’état d’avancement des recherches sur l’autorégulation en psychologie industrielle/organisationnelle (IO). Ils soulèvent des problèmes de validité interne et de construction pour certains concepts clés, tels que le nivcau du but, l’efficacité personnelle, le feedback et la divergence; ils mentionnent aussi qu’il est difficile de préciser quelles sont les facteurs à l’origine des résultats obtenus par les interventions relevant des principes de l’autorégulation. Leurs suggestions concernant les élaborations théoriques, la mesure et la structuration des études sont pertinentes et, si on les met en œuvre, elles permettront à la psychologie IO d’échapper aux problèmes qu’est susceptible de soulever l’élaboration, sur des bases insuffisantes, d’un corpus de connaissances appliquées. A travers leur approche critique, Vancouver et Day font nécessairement un choix dans la littérature qu’ils analysent et ils ne proposent pas de schéma d’ensemble des recherches sur l’autorégulation en psychologie IO. ) present a critical analysis of the current state of self‐regulation research within IO psychology. Their analysis identifies problems of construct and internal validity for certain key constructs, including goal level, self‐efficacy and feedback discrepancy, and difficulties in detecting the sources of effects in studies of interventions based on self‐regulatory principles. Their suggestions regarding theory development, measurement, and study design are timely and, if implemented, will help IO psychology avoid the potential problems of building a body of applied knowledge based on weak foundations. In their critical approach, Vancouver and Day are necessarily selective in the literature they discuss and they do not provide a comprehensive framework of self‐regulation research in IO psychology.
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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 | 0.031 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.040 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".