Bibliographic record
Abstract
Ce dossier spécial vise à cartographier les récentes recherches sur le sport en lien avec la communication pour faire état, en français, des dernières avancées théoriques et analytiques au sein de ce champ d’étude en développement. En rassemblant des chercheurs issus de plusieurs disciplines en sciences humaines, ce numéro interroge différents enjeux corollaires à la médiatisation du sport. L’objectif est d’aborder une variété d’objets médiatiques liés au milieu du sport et, par le fait même, d’y souligner les potentielles contributions aux études en communication. Ce numéro constitue une opportunité pour approfondir les transformations actuelles qui affectent notamment les industries culturelles, le milieu du journalisme, les rapports de convergence et les cultures de consommation, etc. en lien avec le milieu du sport. This special issue aims at mapping the francophone field of research on sport and communication, focussing on its most recent theoretical and analytical developments. Bringing together researchers from various disciplines of the social sciences and the humanities, it probes the relationship between sport, communication and the media. The main goal here is to consider diverse media/sport objects and show how they can contribute to the development of communication as a growing field of research. This special issue constitute an opportunity to understand critically the current transformations affecting notably consumers' culture, dynamics of convergence, journalism, the cultural industries through the sport/media complex.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.111 | 0.025 |
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".