Pourquoi joue-t-on des tours ? Le cas de la mystification en Acadie
Bibliographic record
Abstract
La mystification, une coutume universelle qui consiste à tromper les gens en abusant de leur crédulité, fut particulièrement vigoureuse en Acadie au xxe siècle ; sa présence dans les grands domaines de la tradition, fêtes calendaires, rites de passage, activités quotidiennes et littérature orale, témoigne de la vitalité de cette pratique. Malgré la ténacité et la popularité de la mystification, très peu de chercheurs se sont, jusqu’à ce jour, penchés spécifiquement sur ce sujet, notamment dans la francophonie. Ainsi, j’ai répertorié et analysé les anecdotes racontant les divers tours joués en Acadie afin de saisir l’essentiel de la coutume. Cet article se concentre sur un aspect particulier de la mystification ; illustré par des exemples, il donne un aperçu des motifs communs pour lesquels on joue des tours.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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, 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".