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Record W2986106465 · doi:10.1080/07053436.2019.1682251

Urban skateboarding, social enterprise groups, and community capacity-building in the San Francisco Bay area

2019· article· en· W2986106465 on OpenAlexvenueno aff
Matthew Atencio, E. Missy Wright, Becky Beal, ZáNean McClain

Bibliographic record

VenueLoisir et Société / Society and Leisure · 2019
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsnot available
Fundersnot available
KeywordsSocial capitalEthnographySociologyCommunity organizationDemocracyCommunity developmentEconomic growthCapital cityBayPublic relationsGeographyPolitical scienceSocial scienceEconomic geographyAnthropologyArchaeologyEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.310
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

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