L'évaluation des compétences essentielles pour les gens de métier de l'Ontario (CEGMO) [ressource électronique]
Bibliographic record
Abstract
Le projet L'evaluation des competences essentielles pour les gens de metier de l'Ontario (L'evaluation des CEGMO) visait a determiner si la disponibilite et l'utilisation d'un outil en ligne d'evaluation des competences essentielles au cours de la premiere etape d'un programme d'apprentissage allaient permettre aux participants au projet d'obtenir de meilleurs resultats scolaires pendant leur formation subsequente en classe. Le projet L'evaluation des CEGMO donne suite aux preoccupations exprimees par les enseignants des colleges selon lesquelles bon nombre d'apprentis ne possedent pas les competences de base en mathematiques, en lecture et en utilisation de documents requises pour reussir a l'ecole et sur le marche du travail. A l'heure actuelle, ces lacunes en matiere de competences essentielles ne peuvent pas etre relevees ou comblees avant le debut de la formation en classe de l'apprenti et, meme a ce moment-la, cette tâche s'avere souvent difficile. - Tire du doc.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".