Les musées et les musées virtuels d’histoire : appréciation, utilisation et effet d’une formation sur la pratique enseignante1
Bibliographic record
Abstract
Une formation sur les ressources éducatives des musées et des musées virtuels d’histoire mises à profit dans l’enseignement-apprentissage est dispensée à de futurs enseignants depuis quelques années. L’article porte sur l’appréciation, l’utilisation et l’effet de cette formation sur la pratique enseignante. L’analyse fait ressortir que la majorité des nouveaux enseignants ont apprécié cette formation, même s’ils utilisent assez peu de telles ressources, en raison de leur situation de survie dans la profession. Pour beaucoup, le fait d’en avoir bénéficié les incite à vouloir éventuellement les utiliser, surtout les ressources en ligne des musées virtuels parmi ceux qui exercent en région ou qui sont compétents dans les technologies de l’information et des communications en éducation (TICÉ).
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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".