APPRENTISSAGE ET ENSEIGNEMENT EN SCIENCES PHYSIQUES / LEARNING AND TEACHING IN PHYSICAL SCIENCES
Bibliographic record
Abstract
Dans cet article, nous tentons de soulever les questions caractéristiques générales de l'apprentissage et de l'enseignement des sciences physiques. L'accent est mis, d'une part, sur le caractère de l'apprentissage qui doit avoir des caractéristiques généralisables, une perspective de développement et une certaine indépendance par rapport aux connaissances scolaires institutionnelles et, d'autre part, sur l'enseignement qui doit suivre de manière créative les engagements des programmes et la compréhension des concepts et phénomènes connexes. Dans un tel contexte général, les problèmes ouverts sont discutés par les deux parties, mettant en évidence les convergences et les différences. In this article an attempt is made to raise the general characteristic questions of learning and teaching in Physical Sciences. The emphasis is on the one hand on the character of learning which must have generalisable features, a developmental perspective, and a certain independence from institutional school knowledge, and on the other hand on teaching which must creatively follow the commitments of programs and the understanding of related concepts and phenomena. In such a general context, open problems are discussed by both sides, highlighting convergences and differences. Article visualizations:
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".