The Emergence of Clubs and Drugs in Hong Kong
Bibliographic record
Abstract
Over the last twenty years, the dance scene has gradually emerged and developed into a global phenomenon. This phenomenon is expressive and indicative of a distinctive youth culture. Across the world, from Europe to the Americas to Australia, observers have noted the hip and trendy lifestyle in dress, music and setting of the contemporary dance scene (Hunt & Evans 2002). This globalizing dance scene has also been inextricably connected to the use of illicit drugs. As many researchers in Australia, Canada, England, Scotland and the Netherlands have noted (Adlaf & Smart 1997; Forsyth 1996; Lenton, Boys & Norcross 1997; Measham, Aldridge & Parker 2001; Pedersen & Skrondal 1999; Pini 2001; Redhead 1997; Thornton 1995; Wijngaart et al. 1998), psychoactive drugs like ecstasy, amphetamines, cocaine and marijuana have become an integral part of the dance scene for many participants. Ecstasy has been, perhaps, the most widely recognized drug associated with the scene, as users report its ability to stimulate a euphoric and empathetic state, and at the same time “prolong trance dancing” (Beck & Rosenbaum 1994 p. 54). According to observers, this interplay between ecstasy, dance and the environment of the event has made this form of leisure so attractive and popular among youth. As McRobbie (1994, p.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".