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Record W4308412610 · doi:10.5406/26396025.3.2.03

Every Athlete an Anthropologist: John J. MacAloon's Vision for Sport

2022· article· en· W4308412610 on OpenAlexaff
Bruce Kidd

Bibliographic record

VenueJournal of Olympic Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsBruce Power (Canada)University of Toronto
Fundersnot available
KeywordsBoycottBeijingMedia studiesXenophobiaAmbush marketingTRIPS architectureSociologyHatredInternationalism (politics)Context (archaeology)EthnographyPolitical scienceHistoryPoliticsRacismGender studiesChinaLawEngineeringAnthropology

Abstract

fetched live from OpenAlex

Abstract John J. MacAloon's eloquent advocacy of intercultural communication, exchange, and education in Olympic sports echoes and brings contemporary relevance to the internationalism of Pierre de Coubertin. MacAloon argues that the ultimate test of the Olympic Movement should be the way that during its diverse activities, it enables athletes, coaches, game officials, decision-makers, journalists, spectators, and citizens of host cities and countries to leave their comfort zones to engage with and learn from those from other cultures. In the process, he believes, they will gain confidence in speaking out against xenophobia and hatred, no mean contribution in an increasingly divisive world. This personal reflection upon MacAloon's work outlines the core of his ideas as he first presented them in the 1980s and how they were interpreted at the time; recounts the innovative and illuminating ways of his ethnographical research on the road of Asian and Olympic torch relays, at Olympic and Winter Olympic Games, and his field trips with colleagues, activists, and Olympic insiders at colloquia and conferences; and discusses the possibilities and challenges of intercultural education in Olympic sport today, placing them in the context of the recent calls for a boycott of the 2022 Winter Olympics in Beijing.

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 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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.098
GPT teacher head0.442
Teacher spread0.344 · 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 teacher head, 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

Citations0
Published2022
Admission routes1
Has abstractyes

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