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Record W2894274487

Teacher education for and in global contexts: A cross-case comparison of Malaysia, England, and Canada

2018· article· en· W2894274487 on OpenAlexaffabout
Mark Hirschkorn, Marcea Ingersoll, Jeff Landine, Alan Sears, Carri Gray, Lamia Kawtharani-Chami

Bibliographic record

Venue2018 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsCurriculumCertificationPedagogyPolitical scienceStakeholderGlobal educationTeacher educationPublic relationsSociology
DOInot available

Abstract

fetched live from OpenAlex

In a time of increased global mobility, many preservice teachers in Canada are likely to begin their careers in contexts culturally or geographically disparate from where they undertake their initial teacher preparation. This paper examines the impact of global mobility on initial teacher preparation (ITP) in three countries: Canada, England, and Malaysia. Three case studies illuminate the policy contexts and mobility shifts that are shaping ITP programmatic responses. Stakeholder views from teachers, administrators, university program directors and teacher educators provide insights into the individual and system level impacts of these unique contexts. Mobility manifests in multiple ways, and we explore how globally mobile students, teachers, curriculum, institutions, ideas, and certification intersect in these three cases. Similarities and differences across how universities and schools are managing the transition to teaching in times of intensified global mobility will be shared, and relevant implications for Canadian teacher education programs will be highlighted.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.065
Threshold uncertainty score0.470

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0130.003
Scholarly communication0.0040.002
Open science0.0010.004
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 head0.040
GPT teacher head0.369
Teacher spread0.329 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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