Former des citoyens par la délibération publique : une entreprise fragile (États-Unis et France, 1870-1940)
Bibliographic record
Abstract
Résumé Nous nous intéressons ici au développement, dans des régimes démocratiques, d’entreprises d’apprentissage de la citoyenneté consistant à concevoir des espaces au sein desquels les individus viennent échanger collectivement sur des enjeux publics. Nous montrons que des idéaux assez semblables de la discussion publique ont pris forme en France, dans les réunions politiques contradictoires du dernier tiers du XIX e siècle, et aux États-Unis, notamment dans les public forums des années 1920 et 1930 : dans les deux cas, il est attendu des participants qu’ils s’inscrivent dans des normes et pratiques relevant d’une vision rationnelle de la citoyenneté. Cette comparaison révèle ainsi des similitudes importantes quant aux objectifs poursuivis, mais aussi quant aux réussites et échecs des dispositifs. Nous concluons l’analyse par une réflexion sur les leçons que l’on peut tirer de cette histoire croisée pour les projets contemporains en matière de démocratie délibérative.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".