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Record W2292838170

Canadian Teacher Candidates’ Narratives of Their Cross-Cultural Experiences in China

2015· article· en· W2292838170 on OpenAlexaffabout
Minghua Wang

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNarrativeGeneral partnershipTeacher educationChinaPedagogyCross-culturalReciprocalNarrative inquirySociologyPerspective (graphical)Cultural diversityPolitical scienceAnthropologyVisual arts
DOInot available

Abstract

fetched live from OpenAlex

This is a narrative study, exploring the perspectives of Canadian teacher candidates’ cross-cultural learning experiences in China as a result of their involvement in the Reciprocal Learning Program between the University of Windsor in Canada and Southwest University in China. The study builds on my two years of participation as a graduate assistant in the Reciprocal Learning Program, which is part of the SSHRC Partnership Grant Project between Canada and China. This study focuses on five participants’ personal and professional understanding of cross-cultural knowledge. Based on Connelly and Clandinin’s (1988) narrative inquiry, this study finds some changes from the participants’ cross-cultural perspectives. The findings provide insights for developing pre-service teacher education. In addition, the cross-cultural experiences enhanced teacher candidates’ motivation to advance their educational careers and will broaden their future students’ horizons with a global perspective relevant to the increasingly diverse society in Canada.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.283

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0300.007
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0010.002
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 head0.037
GPT teacher head0.314
Teacher spread0.277 · 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 designQualitative
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

Citations3
Published2015
Admission routes2
Has abstractyes

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