Usage(r)s des plateformes : les publics de l’audiovisuel à la demande
Bibliographic record
Abstract
Ce dossier thématique propose d’examiner les plateformes numériques de diffusion de contenus audiovisuels à partir de leurs « publics ». En effet, si la tendance à la « plateformisation » de l’organisation des activités économiques au sein des industries culturelles est étudiée depuis plusieurs années par les chercheuses et chercheurs en sciences de l’information et de la communication, c’est l’analyse des modèles d’affaires de ces nouveaux acteurs de la diffusion (souvent à travers la notion d’intermédiation) qui a surtout retenu l’attention. Ce dossier propose d’apporter un éclairage complémentaire aux travaux francophones contemporains. À travers l’examen de différents types de publics, plateformes et contextes de communication et grâce à des méthodologies variées, les autrices et auteurs montrent combien les pratiques de consommation audiovisuelle « en ligne » s’inscrivent dans une longue histoire du visionnage des films. This Special Issue proposes to examine digital platforms for broadcasting audiovisual content by looking at their “audiences.” If the economic activities within cultural industries are organized for several years towards the broadcasting on digital platforms, it is the analysis of the business models of these new distribution actors (often through the notion of intermediation) which has especially attracted attention of researchers in information and communication sciences. This issue seeks to enlighten francophones contemporary works. By examining different types of audiences, platforms and communication contexts, through the use of a variety of methodologies, the authors show how “online” audiovisual consumption practices are now part of a long history of film viewing.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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".