Au-delà des « vagues » #moiaussi : cinq ans de mobilisation féministe en musique au Québec (2017–2022)
Bibliographic record
Abstract
Cet article dresse le portrait de cinq organisations qui militent pour l’équité en musique au Québec depuis 2017 :MTLWomen in Music, Femmes* en Musique, Lotus collectiveMTLCoop, shesaid.soMTLet le réseauDIG!Différences et inégalités de genre dans la musique au Québec. En s’inscrivant d’abord dans la longue lignée des travaux critiques en historiographie féministe, l’article rend compte de la pluralité des mobilisations féministes et ce, au-delà des « vagues » #moiaussi qui ont ponctué l’actualité musicale québécoise au cours des cinq dernières années (2017–2022). Dans la seconde partie, les autrices détaillent les travaux du réseauD!G, lancé en avril 2021 par Vanessa Blais-Tremblay, et présentent des retombées initiales prometteuses à la fois pour le milieu universitaire et pour les milieux de pratique en ce qui concerne l’épistémologie et les méthodologies de la « musicologie partenariale collaborative féministe ».
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".