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Record W4285589414 · doi:10.1177/00224871221108655

Chinese Preservice Teachers’ Perspectives of Mentoring Relationships in an International Learning Partnership

2022· article· en· W4285589414 on OpenAlexafffund
Lana Parker, Shijing Xu, Chenkai Chi

Bibliographic record

VenueJournal of Teacher Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsUniversity of Windsor
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGeneral partnershipPedagogyPsychologyTeacher educationReciprocalPerceptionValue (mathematics)Professional developmentPolitical science

Abstract

fetched live from OpenAlex

Mentoring is an essential fixture of teacher education. With growing opportunities for international learning exchanges, there is a need to better understand how cross-cultural mentoring can be characterized by reciprocal learning. This study investigated mentee perceptions of the mentoring relationship in an international, cross-cultural teacher education exchange. We conducted research among 19 Chinese preservice teachers who participated in an international teacher education exchange program, exploring their perspectives on the cross-cultural mentoring relationship and mutual learning. Our findings suggest that learning outcomes are improved in a mentoring relationship when there are strong relational ties, opportunities for reciprocal learning, and a greater awareness of cultural complexity. We contend that there is value in supporting the mentoring relationship directly, which has implications for both international exchanges and teacher education programs.

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 score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.004
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.131
GPT teacher head0.445
Teacher spread0.314 · 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

Citations20
Published2022
Admission routes2
Has abstractyes

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