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Record W4317569411 · doi:10.1080/10476210.2023.2166918

What should teacher education be about? Initial comparisons from Scotland and Alberta

2023· article· en· W4317569411 on OpenAlexafffundabout
Paul Adams, Amy Burns

Bibliographic record

VenueTeaching Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsUniversity of Calgary
FundersMinistry of Advanced EducationGovernment of Alberta
KeywordsMandateWorkforceCraftDirectiveContext (archaeology)RealmTeacher educationPolitical sciencePedagogyEducation policyPublic policyPolicy analysisPublic administrationSociologyProfessional developmentPosition (finance)Higher educationPublic relationsLawEconomicsHistory

Abstract

fetched live from OpenAlex

This article empirically examines the ways in which Initial Teacher Education in Scotland and Alberta, Canada, seeks to ‘get students in’, ‘get them out and into the workforce’, ‘get on with teaching future teachers’ and how it should ‘get on with students’. Using Adams’ (2016) policy heuristic, which posits that policy can be discerned in three realms: frame; explanation; and formation, this paper considers the middle realm: that of policy explanation. Here, attempts to position policy through public pronouncement, policy directive, mandate and/or missive are examined in the context of ITE in Scotland and Alberta. By analysing policy explanations, the paper marks out how both jurisdictions should begin to attempt to craft ITE located in career-long, professional learning and development that understands and acknowledges tensions between ITE and later teacher-education phases. Finally, the paper makes a tentative proposal as to what such ITE might hope to achieve and how it might contribute to a well-developed workforce, so that both locations and other jurisdictions might orient initial teacher development.

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)0.009
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 score0.094
Threshold uncertainty score0.685

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0110.006
Scholarly communication0.0050.002
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.054
GPT teacher head0.419
Teacher spread0.364 · 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

Citations2
Published2023
Admission routes3
Has abstractyes

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