Shadowing Electronic Music DJs at Night : Ethical and Embodied Challenges
Bibliographic record
Abstract
This paper proposes nocturnal shadowing as a multimodal, feminist and sensorial method to access embodied knowledge in high-intensity research contexts. Drawing on our ethnographic study of women* and non-binary DJs in Montreal’s nightlife scene, we explore how interviewing and observing at night calls for a profound methodological reconfiguration—one that takes seriously affect, embodiment, and the complex atmospheres of nocturnal cultural labor. Our project combines shadowing (Aumais & Vásquez, 2023), reflexive interviews (Alvesson & Sköldberg, 2009), and affective fieldnotes documented on-the-go in loud, dark, and fluid environments such as nightclubs, festivals, and informal gatherings. Influenced by feminist epistemologies (Ashcraft & Mumby, 2004), sensual ethnography (Warren, 2008, 2012), and affective methodologies (Gherardi, 2023; Pors, 2021), we reflect on the researcher’s entanglement with the field. We investigate how the body becomes both a research instrument and a site of knowledge production, and how senses—touch, hearing, smell, sight—are constantly mobilized and overstimulated. In these contexts, interviewing extends beyond verbal exchange: consent is dynamic, embodied, and fragile; data includes sound, light, crowd affect, and intoxication; relationships with participants are relationally immersive and often blur the lines between researcher and friend. Moments of dancing, hugging, or simply being-there (O’Grady, 2013) become part of a multimodal archive of the field, where vulnerability, fatigue, and emotional resonance shape what can be known and how it is known. We show how DJs manage sound, emotion, and flow as part of their performance of care, and how gendered power dynamics shape their artistic and social navigation. Simultaneously, we reflect on our own methodological negotiations: how to move unnoticed through a crowd, how to write fieldnotes in motion, how to interpret the researcher’s shifting role in emotionally charged environments. By incorporating post-qualitative approaches and focusing on embodied, affective, and non-verbal forms of data, our contribution resonates with the call of this special issue. We argue that nocturnal shadowing offers a valuable lens to study how knowledge emerges from bodies in motion and atmospheres in flux. It pushes interviewing into a space where words are not primary, and where sensemaking is necessarily multimodal, temporal, and felt. In doing so, we advocate for a feminist methodology that does not sanitize the research process but acknowledges its messiness, partiality, and intensity. Our work not only highlights the strategies of resistance and solidarity developed by minoritized DJs, but also calls for research practices that embrace the co-presence of affect, risk, and relation in the generation of situated, sensual knowledge.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".