Urban skating sport: Current research on sports-related urbanity
Bibliographic record
Abstract
The aim of this thematic issue of the Journal Society and Leisure is to initiate, gather together and compare recent research on urban board sports. The intention is not to produce an exhaustive assessment, but rather to measure the changing trends of research in this field. In this respect, Walker (2013) recalls in his academic study that urban board sports, and more particularly skateboarding, are generally studied from relatively precise analytical angles and usually multidisciplinary perspectives, such as the appropriation of urban spaces, the design of practice areas, the subcultures of the practitioners, risk-taking and related injuries, as well as the design or even modification of practice equipment. Questioning these practices, their social role and the myths surrounding them means going beyond hasty opinion and preconceived ideas and, hopefully, including the results of this study in initial or continuing training for students or actors who decide on the future of these practices when they organize how sports audiences are to be taken into consideration, plan the development of equipment or decide on mobility policies. In the management of sports or youth organizations, as in public policies, it would appear that knowledge work is the basis for any policy aimed at democratic leisure, a factor of individual and societal development that is questioned by the relatively recent emergence of urban board sports.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".