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Record W2794398054 · doi:10.1080/14927713.2018.1449134

‘Everything Looks Better from the Seat of a Bike’: a qualitative exploration of the San José Bike Party

2018· article· en· W2794398054 on OpenAlexaffvenue
Jay Johnson, Matthew A. Masucci, Mary Anne Signer-Kroeker

Bibliographic record

VenueLeisure/Loisir · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNarrativeExperiential learningFocus groupPoliticsQualitative researchSociologyMedia studiesPsychologyAdvertisingPublic relationsHistoryPolitical scienceLawArtPedagogySocial scienceBusinessAnthropology

Abstract

fetched live from OpenAlex

The San José Bike Party (SJBP) originated in 2004–2005 as a diverse collection of cyclists joining together for group rides in and around San José, California. By 2007, a core group of organizers intentionally avoided the overt political overtones of the often-controversial Critical Mass ride for a more benign and ‘fun’ gathering. The ride, which currently attracts between 1,000 and 4,000 cyclists, is often organized around specific themes, such as ‘World Cup’ and ‘Superheroes.’ Upon closer examination, however, there seems to be contradictory experiential narratives that do not necessarily align with the organization’s mission statement, Building community through bicycling. The purpose of this paper is to present results from a qualitative examination of the SJBP. Through analysis and thematization of a series of semi-structured interviews, focus group data, and field observations, the complexities of the often contested and negotiated meanings assigned to the event by SJBP participants are presented.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.075
GPT teacher head0.351
Teacher spread0.276 · 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

Citations5
Published2018
Admission routes2
Has abstractyes

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