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Record W2992705709

“Yankees” and “Bluenosers” at the Races: Harness Racing, Group Identity, and the Creation of a Maine-New Brunswick Sporting Region, 1870-1930

2013· article· en· W2992705709 on OpenAlexaboutno aff
Leah Grandy

Bibliographic record

VenueDigitalCommons (California Polytechnic State University) · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicAmerican Sports and Literature
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)Group (periodic table)Collective identityAdvertisingPolitical scienceBusinessLawArtAesthetics
DOInot available

Abstract

fetched live from OpenAlex

Borders, divisions, and connections can be physical or intellectual, and the borders and regions created by the sport of harness racing were large and small, geographical and social. The eastern North American origin and focus of the sport demonstrated the character of the region; the sport had grassroots origins in the region, and expanded to the level of a major spectator sport by the last decades of the nineteenth century. Harness racing in the Maritimes and New England in the nineteenth century demonstrated the social and economic cohesion of the region and helped to solidify group and personal identities. Maine and New Brunswick were an intellectual and physical region which was, in part, defined through harness racing. The author recently completed her doctoral dissertation at the University of New Brunswick. She currently works at the University of New Brunswick’s Harriet Irving Library and is also employed as a stipend instructor.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.413

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0120.011
Scholarly communication0.0040.002
Open science0.0010.002
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 head0.013
GPT teacher head0.201
Teacher spread0.188 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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