Bibliographic record
Abstract
Des études montrent que le recours à l’écrit dans les dispositifs de reconnaissance des acquis fait reculer des adultes, notamment des adultes sans diplôme. Pour ceux qui vont de l’avant, comment se vit le recours à l’écrit, est-il levier ou obstacle? Cet article présente les résultats d’une étude québécoise ayant fait appel à des entrevues semi-structurées et à l’analyse de documents d’archives. Il décrit le recours à l’écrit dans deux dispositifs de reconnaissance des acquis à l’enseignement secondaire. Le premier est offert en formation générale des adultes et s’appelle Univers de compétences génériques. Le deuxième est celui de la reconnaissance des acquis et des compétences (RAC) en formation professionnelle. L’article identifie leviers et obstacles relevant de l’écrit au sein de ces dispositifs. La discussion met en valeur les défis que pose l’écrit au cours du processus de reconnaissance d’acquis.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".