Bibliographic record
Abstract
Plus que tout autre film depuis la série Au pays de Neufve-France (1959-1960), L’Oumigmag (1993) se présente comme une entreprise de dénomination. Nommer revient à exercer trois fonctions sémiotiques. Désigner, en images ou en paroles, consiste à attirer l’attention sur un objet sans considération de sa signification. La dénotation et la connotation interviennent ensuite pour rassembler des prédicats autour de ce même objet : dans le premier cas, le sens reste le même pour tous les observateurs tandis que dans l’autre, il fluctue en fonction de critères individuels. La stratégie discursive de Pierre Perrault se résume à peu près à ceci : ignorer la dénotation de l’animal pour mettre en relief la désignation et les connotations de son individualité. Réussit-il à démontrer qu’il est possible de nommer l’Oumigmag aussi bien en français qu’en langues vernaculaires?
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".