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Record W3104837701 · doi:10.5539/hes.v10n4p116

Professional Experience in Australian Initial Teacher Education: An Appraisal of Policy and Practice

2020· article· en· W3104837701 on OpenAlexvenueno aff
Susan Ledger, Christine Ure, Madeline Burgess, Chad Morrison

Bibliographic record

VenueHigher Education Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationProfessional developmentLegislatureHigher educationPublic relationsGovernment (linguistics)SociologyPedagogyPolitical science

Abstract

fetched live from OpenAlex

Legislative guidelines regulate professional experience within initial teacher education (ITE) but little is known about teacher educators’ perspectives on how these guidelines are operationalised. ITE providers, in collaboration with school partners, implement a range of programs designed to develop pre-service teachers’ ‘classroom readiness’. We examine how current legislation influences the delivery of professional experience, the provision of funding, and support for university-school partnerships and in-school supervision. This analysis highlights ambiguities in the interpretation of the legislative guidelines, creating a disconnection between policy and practice, an over-reliance on the ‘good will’ of key stakeholders, and competing demands between the ‘actual’ and ‘hidden’ costs of professional experience. Without reform of both policy and practice ITE providers will continue to be constrained when attempting to meet regulatory expectations. These findings demonstrate a need for government departments and ITE regulators to work more closely to improve integration of policy and practice for professional experience.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.459
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.252
GPT teacher head0.584
Teacher spread0.332 · 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 teacher head, 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

Citations11
Published2020
Admission routes1
Has abstractyes

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