Bibliographic record
Abstract
Developpe avec succes dans le monde de l'entreprise et la sphere privee depuis plus de trente ans aux Etats-Unis, au Canada, et recemment en Europe, le mentoring reste peu connu en France, ou on le confond souvent avec le coaching. Ce concept vise a etablir une relation d'echange et d'entraide entre un mentor et un mentore, sur la base du volontariat et sans ingerence manageriale. Contrairement aux autres formes d'accompagnement, il recherche moins le transfert de competences que le mieux-etre des participants. Et de surcroit, la performance est au rendez-vous. Quelles sont les specificites, les pratiques et les formes du mentoring ? En quoi differe-t-il du coaching, de la formation, du tutorat ou du conseil ? Comment ses resultats sont-ils evalues ? Qui sont les mentors et les mentores ? Dans ce livre qui faisait defaut, deux experts du domaine apportent les reperes necessaires a ceux qui s'interrogent sur la place de l'individu dans son environnement de travail et sur le meilleur fonctionnement de l'entreprise.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".