Bibliographic record
Abstract
Cet article a pour objectif de clarifier la distinction entre médium et média, notamment lorsqu’elle est utilisée comme outil heuristique, en prenant comme exemple de son application celui du cinéma. La discussion s’y déroule en trois temps. D’abord, une mise au point terminologique montre qu’un certain flou entoure les catégories de médium et de média, ce qui rend leur emploi quelque peu problématique dans un cadre scientifique. Ensuite, une stratégie qu’on pourrait qualifier de « soustractive » est examinée, celle de la « spécificité du médium », couramment employée dans le champ de la critique, mais peut-être pas aussi efficace qu’on le pense dans le cas de notre exemple, celui du cinéma. La difficulté à énoncer des définitions d’essence ayant été montrée, on se tournera alors vers les définitions d’usage, ce qui suppose d’observer, parmi les pratiques quotidiennes du commerce avec les films, celles qui semblent articuler la différence entre médium et média.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".