Quel changement à l’ère numérique? Quelle ingénierie pédagogique pour y répondre?
Bibliographic record
Abstract
À l’ère numérique, apprendre exige un solide savoir-faire informationnel et la maitrise d’opérations cognitives pour repérer, explorer, départager, choisir, lier, croiser, analyser et traiter le foisonnement des contenus. Dans ce contexte éclaté et transformé, les contenus et les processus d’apprentissage ne sont plus les mêmes. L’idée de l’apprenant autonome, très souvent mise de l’avant depuis bon nombre d’années, prend désormais place, impose tout son sens et invite à redéfinir le paradigme de formation. De manière plus concrète, il est question de s’engager dans une réingénierie de la formation qui soit authentiquement ouverte à l’usage du numérique; une réingénierie dont la profondeur et la complexité ne sont pas à sous-estimer; une réingénierie qui remet en cause tant la finalité que les moyens de formation
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".