Navigating Gendered Spaces: Activists' Synergies in Montreal's Electronic Music Scene
Bibliographic record
Abstract
Abstract Data from international journals show that woman* and other minorities continue to be drastically underrepresented in the music industry worldwide and in the electronic music industry in Europe, Canada, and Quebec. Recent work focusing on the contributions of female electronic music DJs and producers also testify to the intersectional difficulties they face. In this chapter, we examine the strategies they deploy daily to make a career in an overwhelmingly male environment by studying the case of the Montreal electronic music scene. To do so, we use qualitative interviews and observations using the shadowing technique and we deploy a gender-as-social-practice approach, which focuses on how people practice gender in everyday life by considering gender not as a stable state or characteristic of people, but as a dynamic process performed in interactions that produce difference. Our research, which runs from 2021 to 2025, aims to find explanations for the persistent underrepresentation of women* in the electronic music world. More specifically, our results highlight the strategies and coping mechanisms our participants mobilize to negotiate their place and identity in the electronic music industry, paying particular attention to the collective aspect of their mobilization and to their feminist practices, such as creating solidarity networks. *People who identify as woman.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.017 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".