Réseaux sociaux et indexation de la musique, vers de nouveaux modes d’évaluation par l’usage
Bibliographic record
Abstract
Cette communication décrit un projet de recherche en cours soutenu par l’ISCC. Le projet se propose d’examiner les problèmes liés à la production sociale des métadonnées de la musique eu égard aux nouveaux dispositifs et nouvelles pratiques numériques l’indexation et les métadonnées de la musique. Sont décrits le contexte et la motivation de l’étude, un bref rappel de concepts relavant de l’étude. La communication se penche par ailleurs sur l’évaluation de la production musicale et sa réception dans le cadre des réseaux sociaux. Le questionnaire ainsi que les premiers résultats sont décrits.This paper describes an ongoing study supported by ISCC. The project examines issues surrounding social metadata creation for music in light of new devices and digital practices, indexing, and music metadata. The context and rationale for the study are described, as well as a summary of relevant concepts. The evaluation of music production and its reception in social networks are also covered. The questionnaire and the preliminary results will be presented.
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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".