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Record W3201275269 · doi:10.1017/pls.2021.22

Does music affect citizens’ evaluations of candidates?

2021· article· en· W3201275269 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 and the Life Sciences · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsMcGill UniversityUniversité de Montréal
Fundersnot available
KeywordsHonestyTraitPerceptionCompassionPsychologyAffect (linguistics)Social psychologyCompetence (human resources)Political scienceCommunicationComputer science

Abstract

fetched live from OpenAlex

While some candidates use music in some of their campaign ads to shape individuals' perceptions of their competence or compassion, for example, it is unclear whether the relationship between music and trait perceptions is empirically valid. Considering the importance of knowing where trait perceptions-which represent important determinants of the vote-come from and the extent to which it is possible to manipulate trait perceptions by means of music, this study investigates the effect of music on trait perceptions using data from an online survey experiment conducted between October 30 and November 12, 2020. In this experiment, 362 individuals were exposed to a random sequence of five campaign ads, either with their original music or with no music. Following each campaign ad, individuals were asked to evaluate the candidate's competency, honesty, leadership, and compassion. The analyses reveal that music marginally affects perceptions of competency, honesty, and leadership. Moreover, music exerts no significant effect on perceptions of compassion.

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)0.000
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.082
GPT teacher head0.332
Teacher spread0.250 · 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