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Record W2989953029 · doi:10.1123/shr.2019-0006

Women’s High-Wheel Bicycle Racing in Nineteenth-Century America: More than Salacious Entertainment

2019· article· en· W2989953029 on OpenAlexaff
Matt Hall

Bibliographic record

VenueSport History Review · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEntertainmentRace (biology)AthletesAdvertisingGender studiesHistorySociologyPsychologyPolitical scienceLawBusinessPhysical therapyMedicine

Abstract

fetched live from OpenAlex

During the nineteenth century in North America, a small group of working-class women turned to sport to earn a living. Among them were circus performers, race walkers, wrestlers, boxers, shooters, swimmers, baseball players, and bicycle racers. Through their athleticism, these women contested and challenged the prevailing gender norms, and at the same time expanded notions about Victorian women’s capabilities and appropriate work. This article focuses on one of these professional sports, namely high-wheel bicycle racing. Bicycle historians have mostly dismissed women’s racing during the brief high-wheel era of the 1880s as little more than sensational entertainment, and have not fully understood its importance. I hope to change these perceptions by providing evidence that female high-wheel racers in the United States, who often began as pedestriennes (race walkers), were superb athletes competing in an exciting, well-attended, and profitable sport.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.267
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2019
Admission routes1
Has abstractyes

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