Brother, Can You Spare a Seat? Developing Recipes of Knowledge in the Ticket Scalping Subculture
Bibliographic record
Abstract
Although deviance on and off the field have become popular topics of sociological investigation, sociologists have not studied the criminal or otherwise “deviant” roles ticket scalpers play in the cultural spectacle that is professional sport. In many ways, ticket scalpers have become a mainstay part of the backdrop upon which fans experience sporting events. As a first step in developing a sociological portrait of the social processes involved in ticket scalping, it is essential to examine how scalping is accomplished and experienced by its practitioners. This paper is intended to introduce sociologists of sport to die subculture of ticket scalpers by attending to the in-group perspectives and understandings ticket scalpers share toward their practices. Specifically, using ethnographic data collected on 54 ticket scalpers in a central Canadian city, I address how scalpers develop recipes of knowledge for their trade and how ticket scalping is accomplished as a social practice.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".