L'analyse De Contenu, Une Voie D'or Pour L'analyse Des Politiques Educatives? Etude De Cas Du Programme d'Histoire et Education a la Citoyennete et De Sa Controverse
Bibliographic record
Abstract
Cet article propose une analyse de l’influence des medias dans le cycle d’elaboration des politiques educatives en s’appuyant sur une etude de cas portant sur la controverse de 2006 entourant le programme d’Histoire et education a la citoyennete de deuxieme cycle du secondaire au Quebec. Nous y soulignons l’importance des medias dans l’emergence du debat au sein de la sphere publique, l’alimentation et l’amplification de ce dernier, ainsi que son incidence directe sur la decision de revision du programme par le gouvernement du Quebec. Pour ce faire, nous procedons a une conceptualisation de la controverse, laquelle est decomposee en cinq grands enjeux se trouvant chacun au centre d’une confrontation entre deux dimensions. C’est a partir de cette conceptualisation que nous etablissons notre grille d’analyse, puis procedons a une analyse de contenu de la version preliminaire du programme, de sa seconde version et d’une trentaine d’articles de journaux publies entre le debut de la controverse et la presentation de cette seconde version. Cette analyse nous permet d’observer que sur le plan quantitatif, la controverse a eu peu d’effet, alors que sur le plan qualitatif, nous pouvons observer quelques changements subtils, mais importants.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".