Bibliographic record
Abstract
La recherche d’une information connue a priori nous empêche de regarder la réalité telle qu’elle est. Dans le cinéma documentaire, cela donne lieu à une stratégie discursive allant à l’encontre de la recherche d’objectivité. Peut-être passerions-nous à côté de l’essentiel en croyant que L’Oumigmag (1993) porte vraiment sur le boeuf musqué : il s’agit, en fait, d’un essai sur le sens d’un animal emblématique, authentique et marginal qui s’inscrit dans la lignée des Alexis Tremblay, Hauris Lalancette et Didier Dufour. Dans le film, l’auteur y soutient la thèse d’une distorsion de la connaissance par la culture, tout en convoitant l’innocence du regard du chasseur primitif. Sous forme de métaphores, l’interprète se voit soumettre une série d’hypothèses destinées à percer l’énigme des méfiances, de la sauvagerie et des cornes. Reste à savoir jusqu’à quel point le procédé atteint son but, tout indiquant que le projet était d’avance voué à l’échec.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".