MétaCan
Menu
Back to cohort
Record W2620032356

Global Context for Canadian Teacher Education

2017· article· en· W2620032356 on OpenAlexaffabout
Mark Hirschkorn, Alan Sears

Bibliographic record

Venue2017 Conference of the Canadian Society for the Study of Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsContext (archaeology)Political scienceTeacher educationWork (physics)Comparative educationPedagogyInternational educationPublic relationsSociologyEducation policyHigher educationHistoryLawEngineering
DOInot available

Abstract

fetched live from OpenAlex

Due to the provincial nature of the enterprise, policy and scholarly discussion of education in Canada rarely takes place at a national level. Relatively recently, however, the Canadian Association for Teacher Education (CATE) has facilitated the publication of a number of polygraphs bringing together the work of scholars of teacher education from across Canada to provide a relatively rich portrait of initial teacher education (ITE) throughout the country. The current development of an edited book on the history of teacher education in Canada will fill out this picture. This paper draws on all of that work and an extensive review of international trends and policies in ITE to set Canadian teacher education in an international context. Specifically, it identifies a number of key themes in the international literature: the influence of cultural factors on ITE; variations in policy related to ITE; the range of providers of ITE; balance in ITE between academic, pedagogical and applied elements of programs; and the challenges facing ITE for the future, and assesses how they play out in a Canadian context. Participants in the symposium will be asked to discuss how these themes are manifested in their own ITE context.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.829
Threshold uncertainty score0.961

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.009
Science and technology studies0.0260.007
Scholarly communication0.0120.003
Open science0.0010.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0280.001

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 head0.169
GPT teacher head0.410
Teacher spread0.240 · 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 designNot applicable
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
Published2017
Admission routes2
Has abstractyes

Explore more

Same venue2017 Conference of the Canadian Society for the Study of EducationSame topicEducator Training and Historical PedagogyFrench-language works237,207