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Record W2911090768 · doi:10.1177/1012690218819966

He’s ours, not yours! Reinterpreting national identity in a post-socialist context

2019· article· en· W2911090768 on OpenAlexaboutno aff
Sunčica Bartoluci, Mojca Doupona

Bibliographic record

VenueInternational Review for the Sociology of Sport · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
FundersJavna Agencija za Raziskovalno Dejavnost RS
KeywordsNationalismNational identityCitizenshipContext (archaeology)PoliticsPolitical scienceGender studiesIdentity (music)SociologyMedia studiesLawHistoryAesthetics

Abstract

fetched live from OpenAlex

This paper focuses on the relationship between sport, national identity and the media in the post-socialist nation-states of Croatia and Slovenia. It describes what has changed during the eight years since Jakov Fak, a Croatian-born Slovenian biathlete, changed his citizenship and began competing for the Slovenian national team. It also examines how the perception of Jakov Fak as an athlete and of his success has changed through time in different socio-political circumstances – in 2009 and 2010 when he competed for Croatia, and after 2010 when he began competing for Slovenia. To analyse this case we have used different media interpretations of Jakov Fak case, analysing four sports events: the Biathlon World Championships in South Korea (13–22 Feb 2009) and Germany (1–11 Mar 2012), and the Olympic Games in Canada (11–18 Feb 2010) and South Korea (9–25 Feb 2018). The results of discourse analysis show that in the case of Jakov Fak in the years 2009 and 2010, the public was provoked by and exposed to national symbolism, especially in political discourse. The media discourse did change between 2012 and 2018, and discourse typical of civic nationalism began to dominate. Two types of nationalism are mixed in a post-socialist context.

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)0.004
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.419
Teacher spread0.373 · 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 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

Citations11
Published2019
Admission routes1
Has abstractyes

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