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Record W1978739405 · doi:10.1111/cura.12052

Chinese Family Groups' Museum Visit Motivations: A Comparative Study of Beijing and Vancouver

2014· article· en· W1978739405 on OpenAlexaboutno aff
Jiao Ji, David P. Anderson, Xinchun Wu, Changyun Kang

Bibliographic record

VenueCurator The Museum Journal · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsBeijingChinaChinese familyPerceptionGeographyPsychology

Abstract

fetched live from OpenAlex

Abstract This comparative study explored Chinese family groups' dominant visit motivations in science museums and aquariums in order to understand the perceptions of these audiences, who are an under‐represented cultural demographic in the literature. In this study, 503 Chinese participants—131 in the China Science and Technology Museum, Beijing; 127 in the Beijing Aquarium, Beijing; 136 in Science World British Columbia, Vancouver; and 109 in the Vancouver Aquarium, Vancouver—completed a Family Group Visit Motivation Questionnaire. The results report four dominant visit motivations for these Chinese family groups. Significant differences in a fifth motivation, social interaction, were detected in comparing the Beijing and Vancouver Chinese family samples. Also, Chinese family groups were more likely to perceive science museums to be settings that can satisfy their educational and personal interest needs, compared to aquariums. This study provides insights for science museums and aquarium practitioners to better understand this audience demographic.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.412
Threshold uncertainty score0.829

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0070.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.042
GPT teacher head0.268
Teacher spread0.226 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2014
Admission routes1
Has abstractyes

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