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Record W1583620354 · doi:10.1177/0261927x12463005

Measuring Implicit and Explicit Attitudes Toward Foreign Accented Speech

2012· book-chapter· en· W1583620354 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

VenueJournal of Language and Social Psychology · 2012
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsWestern University
Fundersnot available
KeywordsImplicit-association testPsychologyImplicit attitudeStress (linguistics)CognitionSocial psychologyDivergence (linguistics)Cognitive psychologyForeign languageSocial cognitionLinguistics

Abstract

fetched live from OpenAlex

This study applies concepts and methods from the domain of Implicit Social Cognition to examine language attitudes toward foreign and U.S. accented speech. Implicit attitudes were measured using an Implicit Association Test (IAT) that incorporated audio cues as experimental stimuli. Explicit attitudes were measured through self-report questionnaires. Participants exhibited a pro-U.S. accent bias on the IAT measure but a pro-foreign accent bias on explicit measures. This divergence supports the conclusion that implicit and explicit attitudes are separable attitude constructs resulting from distinct mental processes and suggests that language attitudes research—which has traditionally measured only explicit attitudes—would benefit by incorporating indirect measures. The Associative-Propositional Evaluation Model is proposed as a comprehensive and consistent theory to explain the cognitive processing of language attitudes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.667
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.108
GPT teacher head0.311
Teacher spread0.204 · 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