Bibliographic record
Abstract
There has been a significant increase in the number of women DJs since the turn of the twenty-first century due to advances in technology resulting in increased access to cheap or free digital music and software, inexpensive and user-friendly hardware (such as controllers), and the networking and promotional opportunities afforded by the Internet. Moreover, emergent local, regional, national, and international initiatives to advance women in DJ culture are converging with established underground networks and actions, resulting in the increased visibility and influence of BIPOC and womxn DJs and producers, who continue to organize collective resistance to misogyny, sexism, racism, and heteronormativity within DJ cultureboth generally, and specific to Canada. This multi-dimensional studyconducted from the insider perspective of a professional DJexplores the ways in which Canadian women DJs positionality in DJ culture is impacted by the social construction of gender, race, and sexuality. Particular attention is paid to the effects of homosociality and heteronormativity on womens engagement with DJ technologies, and how we resist these forces by forming networks to establish our own physical and digital spaces in Canadian DJ culture. Although womens access to DJ culture and our representation within the culture in terms of media portrayal, diversity, and sheer numbers has improved, the underground and activist scenes propelling these institutional changes are increasingly vulnerable to commercial cooptation that threatens to dilute any revolutionary potential. This study analyzes how women have been excluded from the majority of academic and popular culture discourse on the history of DJ culture, and the importance of documenting our contributions in order to push for a reconfiguration of this history. The research design for this project consists of a mixed-methods approach incorporating qualitative and quantitative data generated from an online survey of 113 womxn DJs, personal interviews with thirty-five womxn DJs, and participant observation. This rich ethnographic data is explored in detail throughout this study.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.030 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.004 |
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".