Bibliographic record
Abstract
In this article, I explore the ways participants in underground dance music (UDM) scenes learn about the political, historical, and social context of their leisure pursuit, such as its queer-of-colour roots, gender dynamics, and economic structures. Semi-structured interviews and information horizon mapping with three Toronto UDM scene members revealed that the online and physical spaces of UDM served as information grounds for the participants’ initial serendipitous discovery of social context information, which led to further intentional seeking in books, libraries, and beyond. Established UDM information sources and normative expectations shaped the participants’ information behaviour, leading them to infer the political stances of DJs and other scene participants based on musical taste and identity. Finally, social context information shapes the participants' experiences of UDM, affecting their sense of community, safety, and belonging. The serious leisure perspective has thus far focused on information behaviours related to the pursuit of the hobby itself, such as, in the present context, finding events or learning to DJ. This study opens up a new direction by considering how social context information flows through and shapes the social world of the hobby. As rising mainstream awareness of structural oppressions leads to more people and communities considering how these oppressions manifest in every aspect of their lives, this perspective will be germane to information behaviour studies in several contexts.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".