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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".