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Record W2774255392 · doi:10.1177/0263395717737887

Voting in the Eurovision Song Contest

2017· article· en· W2774255392 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePolitics · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversité de MontréalMcGill UniversityUniversity of Ottawa
Fundersnot available
KeywordsCONTESTVotingPolitical sciencePoliticsAdvertisingLawBusiness

Abstract

fetched live from OpenAlex

The Eurovision Song Contest is not only the largest song contest worldwide but also probably the world’s largest election for a non-political office. In this article, we are interested in the voting behaviour of Eurovision viewers. Do they vote sincerely, strategically according to rational choice assumptions (i.e. for the song they believe will be the likely winner) or for another song? Using data from a large-scale survey carried out in Europe, we find interesting voting patterns with regard to these questions. Roughly one-fourth of the survey participants would vote for either their preferred song or for the song they think will win. However, the percentage of strategic voters is lower (11%). In contrast, many individuals (i.e. 36% of participants) would vote for another song, one that is neither their preferred song, the likely winner, nor a rational choice. The reasoning behind these remaining votes may include neighbourhood voting, ethnic voting, and voting for one’s favourite European country.

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.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.123
GPT teacher head0.428
Teacher spread0.305 · 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