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Record W2460351706 · doi:10.16997/eslj.121

‘Papa’ Jack and US Federal Interventions

2016· article· en· W2460351706 on OpenAlexaboutno aff

Bibliographic record

VenueEntertainment and Sports Law Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsChampionLawPaternalismWhite (mutation)Political scienceGovernment (linguistics)SociologyHistory

Abstract

fetched live from OpenAlex

After reviewing the significance of race and celebrity in comparative law, this examination details the legal encounters plaguing former world heavyweight champion boxer, Texan Arthur John (Jack) Johnson. On Boxing Day 1908 ‘Papa’ Jack became the first Afro-American to successfully cross the colour line by defeating Canadian Tommy Burns in the unlikely venue of Rushcutters Bay, Sydney. On returning to the United States, and successfully defending his title against ‘Gentleman’ James J. Jeffries, the federal government commenced a protracted series of legal interventions, virtually ending Johnson’s professional sports career. The legal relationships between the federal ‘white slavery’ and ‘fight film’ prohibitions are documented, illustrating the combination of paternalistic racial, moral and commercial justifications levelled against Johnson and images of his prize-fighting exploits. Various legal and bureaucratic implications of this multi-faceted purge within and beyond early modern sport, entertainment and film are also discussed.

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.154
Threshold uncertainty score0.305

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0140.009
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0150.000

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 head0.023
GPT teacher head0.297
Teacher spread0.273 · 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 designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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