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The Demand for Violence in Hockey

2012· book-chapter· en· W2626120547 on OpenAlex
Duane W. Rockerbie

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOxford University Press eBooks · 2012
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsCasualAttendanceLeaguePsychologyEnforcementCriminologySocial psychologyAdvertisingPolitical scienceBusinessLaw

Abstract

fetched live from OpenAlex

Abstract This chapter presents some revealing statistics about violence in the National Hockey League (NHL), and discusses economic literature on why violence is tolerated in the NHL. Next, it provides some new evidence on the effects of violence on NHL attendance. Casual observation provides mixed evidence about the effects of fighting on attendance in the NHL. The first stream of the hocket violence literature investigates whether greater violence in NHL contests contributes to greater attendance and higher salaries for players who exhibit violent behavior, and the second considers whether greater enforcement of the rules that are meant to deter violent behavior actually do so. It is shown that hockey fans respond mildly positively to greater violence, and that efforts made by the NHL to reduce the frequency and severity of violent behavior have been met with modest success.

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.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.981
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.035
GPT teacher head0.189
Teacher spread0.154 · 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