Réflexions sur la notion de contextualisation des apprentissages en sciences et technologies : significations, apports et dérives potentielles
Bibliographic record
Abstract
"Même s’il n’existe pas de cadre conceptuel clair de la contextualisation dans les écrits en didactique des sciences et dans les programmes, les préoccupations que véhicule cette idée (lien des apprentissages avec des contextes hors de l’école) ne sont pas nouvelles. À titre d’exemple, les écrits de Dewey font une référence claire à cette problématique. Des programmes et des manuels de sciences du début du siècle dernier étaient organisés d’une manière qui tenait compte de cette problématique. Au Canada, les deux exemples suivants illustrent cette préoccupation : a) Everyday Problems in Science (Beauchamp, Mayfield et West, 1948); b) Connaissances scientifiques usuelles et hygiène : 6e et 9e année (les Soeurs de Sainte-Anne, 1938). [...]"
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.076 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".