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Record W6894407293 · doi:10.54094/b-f7a79fd9ce

World Cup! History, Politics, and Art of the Beautiful Game [PDF, E-Book]

2025· book· en· W6894407293 on OpenAlexaboutno aff

Bibliographic record

VenueVernon Press eBooks · 2025
Typebook
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsFootballAppealContext (archaeology)PoliticsFresco

Abstract

fetched live from OpenAlex

This collection of essays provides a multidimensional, interdisciplinary, creative, and colorful view on the meanings and possibilities of thinking football—'the beautiful game'—and its paramount event: the World Cup. It is intended to appeal to academics as well as to everyday experts, those for whom football is more than a sport. But it also wants to be a source that stirs the interest of those who see football just as a curious experience; those who may have heard, in passing, that a new World Cup will be played in the U.S., Canada, and Mexico in 2026. This book has, like a football team, eleven chapters. The approaches, styles, and perspectives differ considerably: From how football is a center piece in politics to its representations in poetry, from gender issues to nationalism, from fictitious wars to real ones provoked by a football match, and from exile to the neo-liberalization of the sport, the authors provide us a multicolor and global fresco of football and the World Cup. Likewise, the selection provides a global perspective on football and the World Cup: views from powerhouses such as England or Argentina, as well as from countries with a very incipient football tradition, such as India and Israel. 'World Cup! History, Politics, and Art of the Beautiful Game' is an invitation to continue to understand and think about one of the most important cultural manifestations of our times; a book that, particularly in the context of the next World Cup in 2026, will appeal to a broad readership, all around the world.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.308
Threshold uncertainty score1.000

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.265
Teacher spread0.233 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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