MétaCan
Menu
Back to cohort
Record W4402117604 · doi:10.61945/cjbar.2024.6.1.07

Innovation and Accountability in Teacher Education: Setting Directions for New Cultures in Teacher Education. By Claire Wyatt Smith and Lenore Adie (Editors), 2018. 340 pp.

2024· article· en· W4402117604 on OpenAlexaboutno aff
SAUTH Syna

Bibliographic record

VenueInsight Cambodia Journal of Basic and Applied Research · 2024
Typearticle
Languageen
FieldPsychology
TopicPsychology of Development and Education
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityPsychologySociologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Wyatt-Smith and Adie’s book introduction makes a strong case that teacher quality is key to better results. Three major teacher education innovations are described in this book. Many international reviews of teacher education and reform attempts have been published in the last decade. This textbook encourages governments to evaluate schools and beginning teacher education applicants (in Australia, both at intake and in national assessment before completion). Thus, this book focuses on curriculum orientation consistency since teacher opinions on curriculum orientation affect curriculum decision-making, teaching methodologies, and strategies (Cheung & Ng, 2000). Teachers are crucial to encourage and help students through varied learning and teaching methods. In a fun learning environment, teachers can help kids acquire values like acceptance and respect (Mak et al., 2018). Wyatt-Smith and Adie emphasize ‘the complex ecologies of teacher education’ (p. 13) using chapter contributors from Scotland, Norway, South Africa, Hong Kong, Singapore, Canada, the USA, New Zealand, and Australia. Traditions and culture mediate global imperatives in teacher education. The book’s worldwide perspectives are motivated by the need to create a scholarly platform for critical teacher education concerns in the 21st century and stimulate fresh, evidence-based thinking.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.401
Teacher spread0.354 · 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 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInsight Cambodia Journal of Basic and Applied ResearchSame topicPsychology of Development and EducationFrench-language works237,207