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Record W2937110677 · doi:10.37119/ojs2019.v25i2.418

Innovating in the Margins of Teacher Education: Developing a Bridging Program for Internationally Educated Teachers

2019· article· en· W2937110677 on OpenAlexaffvenueabout
Randolph Wimmer, Beth Young, Jing Xiao

Bibliographic record

Venuein education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsBridging (networking)PracticumTeacher educationCurriculumPedagogyProfessional developmentMedical educationPolitical scienceSociologyPsychologyMedicineComputer science

Abstract

fetched live from OpenAlex

In this article, we discuss our recent and current efforts to offer an innovative form of ongoing teacher education designed explicitly for Internationally Educated Teachers (IETs), which might be considered a type of in-service teacher education. We share some of the observations of IETs who have completed the Faculty of Education’s Bridging Program at the University of Alberta as well as our own experiences. Aspects of the program’s curriculum are described such as its framework including the organization of a bridging seminar and field experiences/practicum. To provide context, we review relevant policies and the limited but valuable research from other Canadian bridging programs for IETs. We conclude with a discussion of the most significant changes we have made to practices at the University of Alberta and address the issue of sustainability. Keywords: Internationally Educated Teachers (IETs); immigrant teachers; foreign-trained teachers; recertification; bridging programs for international teachers; teacher education; professional education; in-service teacher education for international teachers.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0030.002
Open science0.0030.009
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.029
GPT teacher head0.399
Teacher spread0.370 · 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

Citations5
Published2019
Admission routes3
Has abstractyes

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