L’effet d’interaction des primes contingentes et de la motivation autonome sur la performance dans la tâche, contextuelle et adaptative
Bibliographic record
Abstract
Cet article, en mobilisant la théorie de l’auto-détermination, a pour objectif d’évaluer les effets modérateurs des primes contingentes individuelles sur la relation entre la motivation autonome et la performance au travail. Les résultats de l’étude, sur un échantillon de salariés d’une coopérative vinicole (N=135), montrent que la motivation autonome est positivement reliée à la performance dans la tâche, contextuelle et adaptative. Cette étude supporte également l’hypothèse que les primes individuelles modèrent positivement la relation entre la motivation autonome et les différentes facettes de la performance. Nous pouvons observer que c’est le niveau individuel de motivation autonome qui va déterminer l’effet bénéfique, ou au contraire dégradant, des primes individuelles sur la performance individuelle.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".