Bibliographic record
Abstract
Comment faire l’analyse des musiques de séries télévisées ? Comment la musicologie, discipline historiquement façonnée par des paradigmes souvent positivistes et des hiérarchies esthétiques implicites, peut-elle aujourd’hui interroger ses propres cadres épistémologiques pour accueillir un objet culturel longtemps relégué aux marges du savoir légitime ? S’inspirant des travaux de Philip Tagg, cet article propose de tracer les contours d’une discipline en formation en adoptant une perspective critique. Il met ainsi en regard les premiers discours sur les musiques télésériales avec les études émergentes sur le sujet au début des années 1990, soulignant la persistance d’une forme de déconsidération, opérée jusque dans les approches comparatistes qui réduisent ces musiques à un simple dérivé des musiques de film. L’article propose enfin de déconstruire cette distinction artificielle et plaide pour une musicologie qui intègre les singularités formelles et narratives des musiques télésériales.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".