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Record W2594159640 · doi:10.11575/ajer.v62i2.56221

Pan-Canadian Perspectives on Teacher Education: The State of the Art in Comparative Research

2016· article· fr· W2594159640 on OpenAlexaffabout
Adriana Morales Perlaza, Maurice Tardif

Bibliographic record

VenueUniversity of Calgary · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicEducational Practices and Policies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsProfessionalizationPolitical scienceDocumentationHumanitiesSociologyComparative researchPedagogyLibrary scienceSocial scienceArtLaw

Abstract

fetched live from OpenAlex

This text proposes a comparative analysis of the inter-provincial developments of the professionalization of teacher education in Canada, and focuses on two issues: governance of teacher education and the development of new training programs. More specifically, based on a literature review, we analyse how current comparative research brings an understanding of the professionalization of teaching in Canada and we argue the need for more comparative studies in this matter. Ce texte propose une analyse comparative interprovinciale des évolutions de la professionnalisation de la formation des enseignants au Canada selon deux enjeux : la gouvernance de la formation des enseignants et la mise en place des nouveaux programmes universitaires. Plus spécifiquement, nous étudions, à travers une revue de la documentation, de quelle manière la recherche comparative actuelle permet de mieux comprendre le mouvement de professionnalisation au Canada. Nous argumentons enfin de l’importance de réaliser davantage d’études comparatives dans ce sujet.

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)0.011
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score0.952

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0150.043
Science and technology studies0.0170.030
Scholarly communication0.0160.007
Open science0.0030.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0080.000

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 head0.081
GPT teacher head0.373
Teacher spread0.292 · 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 designObservational
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

Citations8
Published2016
Admission routes2
Has abstractyes

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