Le Leader Positif : se transformer pour favoriser la passion et le bien-être au travail
Bibliographic record
Abstract
Cet article propose de diriger notre regard vers la psychologie positive appliquée aux organisations afin de dépasser le modèle de leader taylorien qui repose sur le commandement, le contrôle et l’exécution. Notre démarche repose sur les questions suivantes : Le leader positif favorise-t-il le développement de la passion et du bien-être au travail ? Si oui, comment expliquer ces liens ? Nous essayerons de préciser le processus d’une telle dynamique dans le cadre de l’OCP Répartition qui est un acteur majeur dans la chaîne de distribution de produits de santé. Le dispositif de recherche mêle une méthodologie qualitative et quantitative. Le but est de favoriser la transformation des leaders eux-mêmes et des collaborateurs sur le long terme en contribuant à leur bien-être et à leur efficacité .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".