Métadonnées et répertoire musical québécois : un essai de mobilisation des connaissances dans le nouvel environnement numérique
Bibliographic record
Abstract
Digitized music must contain metadata in order to be licensed, searched, tracked and paid for online usage. Whether factual, cultural (tags and folksonomy), contractual-time-sensitive, content based or usage generated, new norms and standards are being defined to aknowledge the increasing role of music metadata. This research sets out the results of a survey conducted on metadata in the field of sound recording, This first research project is complemented by a second which focuses more specifically on the knowledge mobilization used in carrying out this research, and the construction and transmission of the results. Beyond this formal academic framework, the project involves a phase of mobilization and implementation which has as its aim the development of the TGIT technical and software capability. The overall aim of the project is to meet the practical needs of industry partners, and, as a logical consequence of research-action, these partners will participate to the new phases of the project. For the purposes of our study, we have created a list of musical databases which are already in use. At the time of publication, the list contains 60 databases. For the purposes of our study, we have also created a cross-referenced table of metadata fields in order to compare the constants and methodological differences of certain stakeholders. At present, this table brings together and compares more than 280 fields of databases. Both charts are available online under Creative Commons BY-NC-SA, and will be updated repeatedly as the work progresses.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".