‘Understand us before you end us’: regulation, governmentality, and the confessional practices of raving bodies
Bibliographic record
Abstract
In this article I investigate how power is (re)produced on and through the body, specifically on Toronto's raving bodies during the summer of 2000. Toward the end of 1999 and throughout 2000, Toronto's rave culture came under intense surveillance by institutional and discursive authorities such as city councillors, police, parents, community health organisations, public intellectuals, and the mass media. What ensued was a temporary ban of raves in Toronto on city-owned property. In response to this ban, Toronto ravers relied on liberal approaches such as educational programmes and state lobbying as a way to protect their ‘freedom to dance’. In light of these reactions, one of my primary questions is: As rave becomes more normative, what are its own disciplinary mechanisms or techniques of control that are asserted at the site of the raving body?
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.120 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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 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".