Urban skateboarding, social enterprise groups, and community capacity-building in the San Francisco Bay area
Bibliographic record
Abstract
Urban skateboarding’s current community includes a diverse range of participants and an ever-increasing array of “social enterprise” conglomerations involving city councils, private industries, and nonprofit organizations. We conducted a three and a half-year ethnographic study of San Francisco Bay Area skateboarding, in part, to understand how these evolving public-private entities use skateboarding to build “community relationships”. Research suggests that social enterprise groups aim to develop social capital in underserved urban contexts. Community capacity development encourages social capital, but more intentionally addresses the emergence of socially inclusive and democratic values as well as socially aware learning cultures to benefit youths and their local communities. We used two Oakland, California, case studies of For the Town (FTT) skateboarding and the Skate Like a Girl (SLAG) organization, to exemplify how community capacity-building strategies and practices may occur within urban skateboarding, while also highlighting challenges to this type of 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 | 0.001 | 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 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".