Information municipale et lecture citoyenne : hors-normes et normalité des processus interprétatifs dans des textes ouverts
Bibliographic record
Abstract
L'information municipale est approchée du point de vue de l'interprétation empirique, en termes de lecture citoyenne, une reconstruction de sens d'extraits de procès-verbaux, avis publics ou encore communiqués, manifestant la participation discursive des lecteurs au sens des informations véhiculées.L'articulation à la problématique du hors-normes repose sur le repérage et l'identification de stratégies interprétatives qui révèlent, particulièrement selon le modèle de compréhension intégration de Kintsch et Van Dijk, une expression des connaissances, valeurs, expériences, logiques de pensée des lecteurs-compreneurs à la base de texte.Nos observations conduisent notamment à restituer aux lectures citoyennes de ces écrits professionnels, le caractère transitoire alloué aux textes littéraires hors-normes, car la part hors-normes des stratégies de reconstruction de sens relève d'une logique sociocognitive narrative où la dénomination des acteurs, des actions, de leurs motifs et incidences permettrait aux citoyens de questionner la pertinence sociopolitique des décisions.Où le hors-normes des discours interprétés s'inscrirait dans le genre et présagerait quelque changement possible au pallier de gestion politique le plus proche des citoyens.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".