Numérique, éducation et forme scolaire : enjeux d’équité
Bibliographic record
Abstract
Ce texte s’intéresse principalement aux enjeux d’équité liés aux usages éducatifs du numérique. On distingue d’abord inégalités, iniquités et fractures numériques en prenant comme point d’entrée les différences d’alignement se manifestant entre les systèmes scolaires des pays développés et les usages dont la recherche a montré qu’ils pouvaient jouer un rôle émancipateur. Les pratiques intensifiant l’expérience de participation des élèves sont illustrées par deux cas québécois – l’initiative « École en réseau » (EER) et le projet « L’évaluation collaborative réussie des apprentissages par le numérique (L’ÉCRAN). Les enjeux et les tensions repérés confirment la nécessité de développer l’agentivité des acteurs et de développer des forums d’échange pluridisciplinaires et pluriculturels, afin de former des collectifs hybrides durables.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".