L'orientation
Bibliographic record
Abstract
Comprend : Évolution et disparités d'orientation en fin de troisième / Isabelle Paulin - L'affectation en académie / Patrick Garotin - Les pratiques d'éducation à l'orientation des professeurs de troisième / Jeanne Benhaïm-Grosse - La territorialisation du processus d'orientation en milieux ruraux isolés et montagnards : des impacts du territoire à l'effet de territoire / Pierre Champollion - Le rôle des professeurs de mathématique et de physique dans l'orientation des filles vers des études scientifiques / Josette Costes, Virginie Houadec, Véronique Lizan - Filles et garçons dans l'enseignement supérieur : permanences et/ou changements ? / Christine Fontanini, Josette Costes, Virginie Houadec - Facteurs influençant l'orientation et le parcours de la troisième au post-baccalauréat d'une cohorte d'élèves de l'académie de Clermont-Ferrand / Jean-François Mezeix, Catherine Grange - Voeux, stratégies et orientations réelles des bacheliers technologiques / Karine Pietropaoli - Orientations et réorientations des bacheliers inscrits dans l'enseignement supérieur / Bernard Convert - Le réseau d'accueil, d'information et d'orientation en Bourgogne : une évolution en marche... / Guy Ferez - Le pôle Rhône-Alpes de l'orientation, exemple d'une coordination régionale des acteurs professionnels de l'orientation / Anne Gauthier
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.014 |
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".