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Record W2793551825 · doi:10.1080/14660970.2018.1425683

Twice lost in translation, or what referee Dattilo really said to Colombo in the greatest upset in World Cup history, England v U.S.A. 1950

2018· article· en· W2793551825 on OpenAlexaff
Osvaldo Croci

Bibliographic record

VenueSoccer and Society · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsHistoryMeaning (existential)FolkloreMedia studiesSociologyPsychologyArchaeology

Abstract

fetched live from OpenAlex

This paper offers an explanation of an episode that took place in a 1950 World Cup game that saw the US defeat England by 1–0. It focuses on a linguistic misunderstanding between Italian referee Generoso Dattilo and US centre back Charles Colombo, an Italian-American from St. Louis. More precisely, the paper offers an explanation of what referee Dattilo said to Colombo following a rugby-like foul committed by the latter on English forward Stanley Mortensen. Colombo, as well as many others who recounted the story afterwards, maintained that the referee, surprisingly, complimented him. This article argues that what Colombo took as a compliment in Italian was instead a warning issued in Roman dialect. The meaning of the referee’s words was lost in translation and Dattilo’s warning turned into an unlikely compliment that has since entered soccer folklore as one of the defining moments in the greatest upset of World Cup history.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.115
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.013
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.105
GPT teacher head0.304
Teacher spread0.199 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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