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The Economics of Discrimination

2012· book-chapter· en· W2622292749 on OpenAlexaboutno aff
Neil Longley

Bibliographic record

VenueOxford University Press eBooks · 2012
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBasketballLeagueAgency (philosophy)Ice hockeyPolitical scienceDemographic economicsPolitical economySociologyEconomicsHistorySocial science

Abstract

fetched live from OpenAlex

Abstract This article replicates for hockey what others have done for baseball and basketball, with the interesting exception that alleged discrimination in hockey falls along the lines of language and national origin rather than along racial grounds. The National Hockey League (NHL) essentially has three minority groups: Americans, Europeans, and French Canadians. The focus in the discrimination literature has been on French Canadians, and, to a lesser extent, Europeans. It is reasonable to suggest that Canadian fans and media have a much stronger sense of “ownership” of the game of hockey than what is found in the United States. There have also been economic changes that have occurred in recent years that should work to decrease any possible discrimination. While the NHL has historically had the least amount of voluntary player mobility, the most recent collective bargaining agreement signed in 2005 provides for a much more liberalized system of free agency.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0200.001

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 head0.040
GPT teacher head0.179
Teacher spread0.139 · 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 designTheoretical or conceptual
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
Published2012
Admission routes1
Has abstractyes

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