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Record W4414195366 · doi:10.3138/uhr-2024-0024

Vancouver’s Veloligarchy: The Role of Cycling Clubs in Early Elite Formation

2025· article· en· W4414195366 on OpenAlexaffvenueabout
John Douglas Belshaw

Bibliographic record

VenueUrban History Review · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsEliteCyclingRecreationClubPower (physics)

Abstract

fetched live from OpenAlex

Histories of the late 19th-century “bicycle craze” tend to focus on events, riders, and the building of better roads for cyclists. Everywhere, elites—who almost exclusively could afford both the cost of a bicycle and the time for riding—were important in these developments. Histories of the haute bourgeoisie, by comparison, tend to understand their nodes of power as exclusive business clubs or lodges and mostly regard sports and recreation as leisure, as social breaks between projects. In Vancouver, Canada, elite formation paralleled the appearance and growth of cycling in the city’s first 20 years. In the absence of other “command centres” in an emergent city, the ambitious gathered power and guided the development of the city through their cycling clubs. This study approaches the integrity of these organizations first through the lens of a manslaughter trial in 1900, at which time the clubs were at their peak. It turns then to the processes of club formation and their various projects and goals in the West End and Stanley Park in the 1890s. A prosopographical investigation follows to demonstrate the extent of connections within these groups, and it closes with a reflection on a decade of cycling clubs’ influence over both elite formation and the shaping of Vancouver.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.805
Threshold uncertainty score0.207

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.028
GPT teacher head0.294
Teacher spread0.267 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes3
Has abstractyes

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