L’exercice du jugement dans les débats publics expertisés : le cas de la reconstruction de l’échangeur Turcot à Montréal
Bibliographic record
Abstract
De quelles façons les acteurs exercent-ils leur jugement, forgent-ils leurs opinions dans les débats publics sur des questions socioécologiques? Quelles possibilités y ouvre le recours à l’expertise, mais aussi quels pièges tend-il? Accorde-t-on un espace suffisant à la confrontation des valeurs qui orientent nécessairement les projets collectifs? À travers l’étude d’un cas, soit celui du débat entourant la reconstruction de l’échangeur Turcot à Montréal, mais aussi l’analyse de contributions théoriques et philosophiques – dont celles de Hannah Arendt relativement aux concepts de responsabilité et de jugement – nous abordons les enjeux éthiques et politiques que soulèvent ces questions et y apportons des éléments de réponse. Nous argumentons notamment en faveur d’une mobilisation de l’expertise qui soit intégrée à une démarche réflexive et critique partagée, en vue du développement d’une société apprenante.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.018 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.000 |
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".