CompeTI.CA : un réseau des partenaires pour développer les compétences en TIC en Atlantique
Bibliographic record
Abstract
Le developpement de competences numeriques sur un continuum vie – education -carriere passe par un partenariat entre differents paliers educatifs, ce qui demontre un projet de construction d’un Reseau (nom du reseau). Suite a un travail collaboratif, depuis 2014, les partenaires ont defini, comme premier objectif, en consultant des experts, differentes facettes de competences numeriques, techniques et non-techniques. Cette definition a permis de mettre en place des etudes de pratiques exemplaires dont les premiers resultats indiquent l’importance de pedagogies ouvertes, axees sur l’apprenant, en lien avec la vie de tous les jours. Ces approches cherchent a maximiser le potentiel de chacune et de chacun en creant des occasions d’apprentissage de vivre et de reussir dans un monde numerique. Les questions de transfert de pratiques exemplaires et de durabilite d’encadrement aux points de transition entre les differents contextes educatifs, formels et informels demeurent ouvertes formant une base de continuite du partenariat par l’echange d’expertise, la formation continue et la recherche longitudinale. Notre presentation fait part de resultats du travail de quatre premieres annees du Reseau et de la perspective future tant au niveau de nouvelles collaborations qu’au niveau de theorisation ancree.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.012 |
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".