Bibliographic record
Abstract
La communication floue, notion basée sur la Théorie de la pertinence de Sperber & Wilson, repose sur l’attribution d’états mentaux. Non seulement, au cinéma, il faut se faire une idée des intentions de qui nous parle (hypothèse développée par Alessandro Pignocchi), mais aussi de ce qu’il y a dans la tête des personnages (hypothèse développée ici). Le flou de la communication par les images animées vient aussi de l’existence du protolangage audiovisuel, qui ressemble aux représentations mentales et relève comme elles, dans ses formes, de la cognition incarnée. De plus il ne s’agit pas seulement de « comprendre » les films ou les séries télé, mais aussi, au quotidien, de les décrire, de les analyser, de les interpréter, d’en faire l’expérience, de les apprécier et d’en avoir usage. Inutile de dire qu’étudier tout ceci ne peut se faire que de manière interdisciplinaire.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".