CHAPITRE 7. Les valeurs explicites dans les programmes et dans les manuels de sciences et technologies au Québec
Bibliographic record
Abstract
On le dit souvent : l’ecole ne vise pas seulement l’acquisition par les eleves d’apprentissages disciplinaires desinteresses. Elle vise aussi a leur transmettre un ensemble de valeurs explicites, mais aussi implicites. Le discours officiel qui a prepare et accompagne la derniere reforme educative au Quebec reconnait l’importance et la place des valeurs, non seulement en education, mais aussi dans les enseignements disciplinaires. Il parait donc pertinent d’analyser toutes les unites de sens qui traitent des valeurs explicites dans les programmes et dans les propos introductifs des manuels scolaires de sciences et technologies du primaire et du premier cycle du secondaire. L’analyse de ces donnees montre que le concept de valeur est tres peu present dans les sections qui concernent les sciences et les technologies alors qu’il est abondamment traite dans les domaines de l’Univers social (sciences humaines) de l’enseignement moral et religieux. Cette analyse souligne la conception predominante selon laquelle les sciences seraient productrices de faits (neutres) sur la nature alors que les disciplines qui traitent de la societe et de la morale seraient responsables de la determination des valeurs.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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".