"Pretty good for a girl" : Navigating gendered spaces as a female electronic DJ and producer
Bibliographic record
Abstract
In 2018, Misstress Barbara, electronic music DJ and producer from Montreal (Quebec, Canada), posted on her Facebook page about how far she had come since her mid-90s debut and mentioned that she had frequently been told throughout her career: ‘You're pretty good, for a girl’. This sexist remark illustrates the challenges women still face in the predominately male-dominated electronic music industry. Data from international journals show that women continue to be drastically underrepresented in the music industry in general but there is little information in the literature about the underrepresentation of women in the electronic music industry specifically (Farrugia & Olszanowski, 2017; Gadir, 2017). \n \nFemale electronic music DJs and producers remain underrepresented at festivals and events worldwide (Abtan, 2016; Gadir, 2017; Hesmondhalgh & Baker, 2015) and less visible in the professional media and production networks, which tend to focus on men (see, for example, Butler, 2006; Holmes, 2012; Kirn, 2011). In Montreal, the practical importance of this issue is highlighted by the numerous groups working to address it in Montreal such as Montreal Women in Music, F*EM, Female Pressure, Amplify Digital Arts Initiative, Keychange, and She Said So Montreal. In Quebec, the overall participation of women in festival programming is around 10% (DIG, 2017). And the participation of women in electronic music festivals such as Île Soniq and Mutek were 23% and 30% respectively in 2019 (Female Pressure, 2020), whereas the average number of university graduates with music degrees was in the parity zone in 2015 (Statistics Institute of Quebec) and around 50% of singers and musicians in Canada are female (Nordicity, 2015, Hill Strategies Research, 2016). This underrepresentation is not unique to the Canadian electronic scene as just under 13% of the musicians scheduled in European electronic music nightclubs in 2019 were female (A8M, 2019). \nOur research aims to find explanations for this persistent under-representation and invisibilization despite a certain increase presence of women on the electronic scene since the 2000s (McRobbie, 1994). Recent work focusing on the contributions of female electronic music DJs and producers also testify to the intersectional (at the intersection of various identities such as ethnicity, gender, sexual identity, age, capacity, etc.) difficulties they face (Farrugia & Olszanowski, 2017). How do we explain this persistent underrepresentation of women in the electronic music world? What are the day-to-day consequences for female DJs and producers? What are the strategies they deploy on a daily basis to make a career in an overwhelmingly male environment? \n \nThis research aims to contribute to a better understanding the under-representation in this industry and, ultimately, to the improvement of the working conditions of these women who actively participate in the development of a flourishing economic field in Montreal, as evidenced by the popularity of festivals created here that have been exported, such as Piknic Electronique, Igloofest and Mutek. We will now explore the theoretical and practical implications of these questions for our research.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".