La musique instrumentale produite au Québec durant la décennie 2010 : établissement d’un corpus et analyse du phénomène
Bibliographic record
Abstract
La musique instrumentale, plus précisément celle destinée à l’enregistrement et secondée par un travail de composition, est au coeur de la présente étude en regard de l’importance qu’elle a acquise dans l’industrie de la musique québécoise depuis la décennie 2010. Des noms comme ceux de Jean-Michel Blais, Martin Lizotte, Marc-André Pépin et Alexandra Stréliski sont au coeur de la popularité qu’a connue le phénomène, bien qu’il suscite autant des réactions positives que négatives eu égard à son appellation et des croisements entre univers classique et populaire. En ce sens, la présente étude délimite un corpus et en propose une analyse tout en faisant ressortir les tendances majeures de cette musique et les défis que sa circulation a rencontrés au sein de l’espace médiatique du Québec.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".