Statistiques linguistiques, rhétorique quantitative et effets de perspective
Bibliographic record
Abstract
La quantification, que l’on peut donc définir brièvement comme la description de phénomènes au moyen de nombres, constitue une démarche familière et indispensable : la question « combien ? » est sans doute l’une des plus courantes que l’on se pose à propos d’à peu près n’importe quoi. Mais les outils mis en oeuvre pour y répondre ne manquent pas d’exercer un effet sur la perception que nous avons des phénomènes quantifiés. On s’interrogera ici sur certains des effets de perspective qui peuvent être générés par des opérations aussi élémentaires que la construction des catégories et la transformation des données en pourcentages. Pour explorer et illustrer ces effets de perspective, nous aurons principalement recours aux statistiques particulièrement chargées sur le plan symbolique que sont celles de la langue, qui ont souvent été, dans le contexte canadien et québécois, la source de controverses et l’objet d’instrumentalisation à des fins politiques.
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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 | 0.030 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".