Bibliographic record
Abstract
Résumé Cet article aborde la question de l’envie au travail, une émotion omniprésente dans les organisations mais absente des théories et des discours de gestion. Prendre en compte l’envie permet de comprendre différemment de nombreux aspects de la vie au travail et d’éclairer sous un nouvel angle certains comportements, problèmes et dysfonctionnements courants dans l’entreprise. S’appuyant sur l’analyse de plusieurs cas, ce texte met en évidence l’omniprésence de cette émotion dans les organisations et ses effets potentiellement destructeurs. Il montre ensuite de quelle manière l’envie peut surgir dans de nombreux actes de management; en effet, le recrutement, la promotion, la gestion des carrières, la réorganisation, l’évaluation de la performance et la répartition des ressources sont autant de domaines qui, parce qu’ils touchent à la place et à la valeur respectives des individus, sont susceptibles d’exciter l’envie. Enfin, l’auteure indique comment éviter que l’envie ne se développe durablement dans un contexte de travail.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".