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Record W3037415613 · doi:10.22215/etd/2020-14083

A Cross-Cultural Examination of Explicit and Implicit Attitudes toward Shyness in Canada and Mainland China

2020· dissertation· en· W3037415613 on OpenAlexaffabout
Bowen Xiao

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsCarleton University
Fundersnot available
KeywordsShynessPsychologyMainland ChinaImplicit attitudeSocial psychologyPersonalityChinaImplicit-association testDevelopmental psychologyAnxietyPolitical science

Abstract

fetched live from OpenAlex

The aim of this doctoral dissertation was to explore explicit and implicit attitudes toward shyness among University students in Canada and mainland China.Study 1 explored differences in normative beliefs about shyness between samples of Canadian and Chinese students.Participant were N = 1417 undergraduate students from Shanghai, People Republic of China (N =850, Mage=18.83 years, SD = .92)and Ontario, Canada (N= 567, Mage=19.7 years, SD = 2.14).Participants were completed assessments of normal belief about shyness and their own personality.Results from Study 1 indicated that, contrary to predictions, shyness was viewed more negatively in China as compared to Canada.As well, shy behaviours were viewed as more acceptable among participants who rated themselves as more shy.The goal of Study 2 and Study 3 was to further explore Canadian students' implicit attitudes about shyness.Undergraduate students (Study 1: N

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.024
GPT teacher head0.355
Teacher spread0.331 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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