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Record W3197159115 · doi:10.1177/00224871211042306

Becoming Globally Competent Through Inter-School Reciprocal Learning Partnerships: An Inquiry Into Canadian and Chinese Teachers’ Narratives

2021· article· en· W3197159115 on OpenAlexafffundabout
Yishin Khoo

Bibliographic record

VenueJournal of Teacher Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of WindsorUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsForegroundingGeneral partnershipPedagogyNarrativeReciprocal teachingSociologyCompetence (human resources)ReciprocalChinaTeacher educationProfessional developmentMathematics educationPsychologyPolitical science

Abstract

fetched live from OpenAlex

This study explores how a Canada–China Sister School Network provides school-based professional learning opportunities for in-service teachers to grow their knowledge and capacity to educate for global competence and citizenship (GCC). In particular, it presents the story of a Canadian teacher and a Chinese teacher who had found ways of educating for GCC through carrying out intercultural and international reciprocal learning in a researcher-supported inter-school reciprocal learning partnership. By inquiring into the Canadian and Chinese teachers’ growth narratives, this study highlights four lessons teachers, educators/researchers, and policy makers may learn from the two teachers. It concludes by highlighting the potential of a relationship-oriented, open-ended, and non-hierarchical international school network in supporting teachers to become more globally competent, foregrounding reciprocal learning and collaboration among school practitioners and researchers.

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 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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.226
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.074
GPT teacher head0.410
Teacher spread0.336 · 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 teacher head, 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

Citations14
Published2021
Admission routes3
Has abstractyes

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