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Record W4417089030 · doi:10.1093/poq/nfaf041

How Can We Size Your Core Issue? Assessing Salience Validity Using Psychophysiology

2025· article· en· W4417089030 on OpenAlex
Camille Tremblay‐Antoine, Yannick Dufresne, François Vachon

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePublic Opinion Quarterly · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsSalience (neuroscience)OperationalizationMeasure (data warehouse)Confirmatory factor analysisPsychophysiologySet (abstract data type)Core (optical fiber)Construct validity

Abstract

fetched live from OpenAlex

Much research in public opinion attempts to operationalize and measure individual issue salience. Measuring this concept presents its own set of challenges, due in part to the fact that studies rely mostly on so-called "subjective" methods to measure the strength of attitudes. This paper aims to transcend the classical methods used in surveys to measure salience by comparing the results of these common approaches with results obtained with physiological measures. Using the Confirmatory Factor Analysis Model with the Correlated Uniquenesses method, correlations between three survey question methods and two physiological measurements are compared to measure individual issue salience. Results show a strong correlation between all the measures tested and therefore add validation to survey approaches used in social sciences to measure issue salience. The results therefore demonstrate that individuals know which issues trigger the most reactions in them.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.154
GPT teacher head0.429
Teacher spread0.275 · 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