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Record W2163603589 · doi:10.1002/jtr.821

Are we really measuring what we think we're measuring? Assessing attitudes towards destinations with the implicit association test

2010· article· en· W2163603589 on OpenAlex
Dae‐Young Kim, Zhijian Chen, Yeong‐Hyeon Hwang

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

VenueInternational Journal of Tourism Research · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImplicit-association testDestinationsImplicit attitudePsychologyTest (biology)Social psychologyChinaTourismAssociation (psychology)Political science

Abstract

fetched live from OpenAlex

ABSTRACT The study examines individuals' attitudes toward destinations by comparing the results of traditional self‐report surveys with those of the implicit association test (IAT). A total of 84 college students (30 Caucasian, 27 Chinese and 27 Korean) were employed to complete self‐report surveys and computer‐based IATs. The results show that participants' attitudes toward selected destinations (i.e. China and England) vary depending on which of the two different attitude measures is employed. Specifically, it appears that attitudes toward the two countries are not significantly different in self‐report survey, but differences in attitudes are significant in the IAT. This result indicates that greater use of the IAT would enhance our understanding of tourist responses, particularly those related to ability and willingness issues. The implications of the IAT results for tourism destination studies and its relation to explicit measures of attitudes are discussed. Copyright © 2010 John Wiley & Sons, Ltd.

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)0.016
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.603
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0030.000
Research integrity0.0000.003
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 head0.143
GPT teacher head0.434
Teacher spread0.291 · 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