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Record W4377252333 · doi:10.1515/9780773553040

Gael Force, Second Edition

2018· book· en· W4377252333 on OpenAlexaboutno aff
Merv Daub

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2018
Typebook
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Football at Queen’s University has one of the richest and longest histories of any sport in Canada. The Golden Gaels have been a presence in Canadian football at both the amateur and professional levels since 1882. Gael Force traces this history, chronicling the team’s ups and downs and integrating them within the history of the university, the country, and the sport in general. Providing a wealth of interesting facts and engaging anecdotes as well as profiles and photographs of the coaches, captains, and players, Merv Daub takes the reader through more than a century of Queen’s football. Drawing from a wealth of sources, Daub recounts the team’s key milestones including their first Dominion championship in 1893 with “Curtis and his boys,” three consecutive Grey Cup wins in the 1920s, the 1934–35 victory of the “Fearless Fourteen,” the 1955 season when Gus Braccia, Ronnie Stewart, Gary Schreider, Lou Bruce, Al Kocman, “Jocko” Thompson, and the rest of that “band of merry men” brought Queen’s back into the limelight, the golden years of the 1960s, and the 1978 and 1992 Vanier Cup championship seasons. Adding twenty more years of football history since Gael Force was first published in 1996, this new edition includes the 2016 season played at the revitalized Richardson Stadium. It is both a tribute to a long-standing football legacy at Queen’s and an important historical and sociological study of college sport in Canada.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.490
Threshold uncertainty score0.728

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.4900.491

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 head0.019
GPT teacher head0.235
Teacher spread0.215 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

Explore more

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