ÉDUQUER À LA CITOYENNETÉ DÉLIBÉRATIVE : LE DÉFI D’EXERCER UN LEADERSHIP SOCIOPOLITIQUE POUR RECTIFIER LES INÉGALITÉS À L’ÉCOLE ET EN DÉMOCRATIE / RECTIFYING INEQUALITIES IN SCHOOL AND IMPROVING DEMOCRACY THROUGH DELIBERATIVE CITIZENSHIP ...
Bibliographic record
Abstract
Les responsables de l’ecole secondaire quebecoise affirment developper la capacite des eleves de deliberer de facon critique a propos d’enjeux sociaux et scolaires au moyen de l’education a la citoyennete. Cependant, la deliberation scolaire peut conforter des inegalites sociales ou politiques interindividuelles. La prise en compte d’un tel obstacle conduit a aborder la question suivante : a quelles conditions l’education a la citoyennete preparera-t-elle les eleves a combler l’ecart entre la realite des injustices sociales et l’ideal de la citoyennete libre et egale ? RECTIFYING INEQUALITIES IN SCHOOL AND IMPROVING DEMOCRACY THROUGH DELIBERATIVE CITIZENSHIP EDUCATION: THE CHALLENGE OF EXERTING A NEW SOCIOPOLITICAL LEADERSHIP In high school, one of the most important stated aims of citizenship education is to develop the capacity to deliberate critically about public and democratic stakes. However, a deliberative community of students might reinforce inequalities based on social or political classes. This obstacle leads us to deal with a challenging question: which conditions in school must be fulfilled so that citizenship education may prepare students to reduce the gap between the ideal of free and equal citizenship, and the not so free and equal social reality?
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.039 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".