Quand les contextes se comparent et se parlent
Bibliographic record
Abstract
L’idée de départ du projet TEEC consiste à intégrer le contexte dans l’apprentissage des sciences et d’étudier les modalités de cette intégration. Face à un enseignement trop souvent détaché du contexte, le rôle de ce dernier pourrait être mis en lumière au bénéfice des élèves en provoquant un choc entre deux contextes contrastés, dans le cadre de l’étude d’une même notion scientifique. Ce projet s’appuie sur des apprentissages collaboratifs synchrones et asynchrones menés par des élèves de Guadeloupe et du Québec. La méthodologie retenue est de type Design-Based Research (DBR), et se matérialise par la mise en œuvre de plusieurs itérations in situ. Ces itérations portent sur l’histoire sociale, la géothermie, l’éducation au développement durable, et le conte. L’enjeu de ce projet de recherche est d’étudier les relations entre modélisation des contextes externe et émergence des effets de contextes.
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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".