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Record W3081690803 · doi:10.1080/09523367.2020.1794835

The <i>Cool Runnings</i> Effect: Flexible Citizenship, the Global South, and Transcultural Republics at the Winter Olympic Games

2020· article· en· W3081690803 on OpenAlexaff
Tom Fabian

Bibliographic record

VenueThe International Journal of the History of Sport · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsCitizenshipContext (archaeology)GeopoliticsLegitimacyPolitical scienceNarrativeState (computer science)GlobalizationAthletesPolitical economyGender studiesSociologyHistoryPoliticsLaw

Abstract

fetched live from OpenAlex

As a pinnacle event in the global sporting calendar, the Summer Olympics have long been a site of flexible citizenship, whereby athletes sell their talents to the highest bidder. This trend in athlete labour migration has created transcultural national teams and threatened the legitimacy of the nation-state within the international sporting system. However, this trend has received little to no attention within the Winter Olympic context, in which the ‘athlete-mercenaries’ tend to flow from the Global North to represent nations in the Global South. This new transcultural representation has not yielded Olympic success, rather narratives of inclusion and participation. Although these Winter Olympic newcomers are considered ‘exotic oddities,’ they raise similar questions about nationhood and citizenship that their summertime counterparts do, albeit in the increasingly geopolitical context of the Global South. Flexible citizenship is an extension of the athlete migration trends that have become synonymous with globalization and economic liberalization and will remain a factor in international sport exchanges as long as the foundational unit of the system is the nation-state.

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 score0.349
Threshold uncertainty score0.469

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.025
GPT teacher head0.269
Teacher spread0.244 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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