Au-delà des « vagues » #moiaussi : cinq ans de mobilisation féministe en musique au Québec (2017–2022)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cet article dresse le portrait de cinq organisations qui militent pour l’équité en musique au Québec depuis 2017 : MTL Women in Music, Femmes* en Musique, Lotus collective MTL Coop, shesaid.so MTL et le réseau DIG! 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 réseau D!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 ».
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.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it