Déstabiliser la restitution du savoir : écritures relationnelles, corporelles et processuelles
Bibliographic record
Abstract
Cet article examine comment les normes d'écriture académique limitent la restitution des savoirs sensibles dans la recherche universitaire. À partir de l'expérience du collectif Compo X, nous analysons trois normes dominantes qui contraignent l'expression scientifique : l'individualisme, la neutralité et la linéarité. Ces exigences de performance académique uniformisent les formes de transmission du savoir et désavantagent les parcours atypiques et les populations marginalisées. Notre démarche d'expérimentation collective, menée à travers des ateliers destinés aux étudiantXes au doctorat, révèle des stratégies concrètes pour « écrire différemment ». Nous proposons trois formes de déstabilisation des normes académiques : les écritures relationnelles, corporelles et processuelles. Ces approches alternatives permettent de renouer avec les dimensions sensibles de la recherche et d'ouvrir de nouveaux espaces d'expression dans le monde universitaire, contribuant ainsi aux microrévolutions par l'écriture. L’article souligne l’importance de créer des espaces de liberté pour permettre une écriture authentique et incarnée, tout en questionnant les critères d’évaluation et la hiérarchie des auteurXrices.
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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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".