Bibliographic record
Abstract
5116).Ses recherches portent sur l'analyse des politiques publiques d'orientation, la territorialisation des politiques d'emploi et la régionalisation de la formation professionnelle.Il a récemment publié : (avec M. Bel) « Proximité et relation emploi-formation : au carrefour des disciplines », Espaces et sociétés (2009), 1-2, pp.136-137 ; (avec P. Iriart) "Vocational Education Policies in the Process of Multilevel Governance: A French Perspective", Bulletin of Comparative Labour Relations, Kluwer Law International, avril 2009, pp.33-46 ; « Les enjeux de l'évaluation territoriale des politiques publiques », Informations sociales, n° 150, octobre-novembre 2008.Bénédicte Gendron est professeur des universités à l'université Montpellier 3 -Sud de la France et est rattachée à l'unité de recherche du Lirdef (Laboratoire interdisciplinaire de recherche en didactique Éducation et Formation), EA n° 3749, et associée au Centre régional associé Céreq Ile-de-France.Ses travaux de recherche s'intéressent au domaine des ressources humaines dans une perspective économique, psychologique et éducationiste.Particulièrement, ses derniers travaux se focalisent sur le capital émotionnel et les liens compétences émotionnelles sociales et personnelles, performance au travail et santé.Ses dernières publications : Gendron B. et Lafortune L. (dirs.)(2009), Leadership et compétences émotionnelles dans l'accompagnement au changement, Ouvrage Collection « Éducation & Recherche », Québec : Presses de l'université du Québec, p. 237.Gendron B. (2009) "The Vocational baccalaureate: a gateway to higher education?",European Journal of Vocational Training, n° 46, 2009/1, pp.5-27.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.270 | 0.072 |
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".