MétaCan
Menu
Back to cohort
Record W2902302614

The Use of Children's Literature in Teaching: A study of politics and professionalism within teacher education

2016· book· en· W2902302614 on OpenAlexaboutno aff
Alyson Simpson

Bibliographic record

Venuenot available
Typebook
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsPedagogyEmpowermentTeacher educationPoliticsLiteracyAutonomyFraming (construction)Agency (philosophy)Professional developmentSociologyPolitical sciencePsychologyMathematics educationSocial scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

The Use of Children's Literature in Teaching reveals the impact of politics, professional guidelines and restrictive measurements of literacy on the emerging identities of young teachers. It places renewed emphasis on the importance of creative teaching with children’s literature for the empowerment of teacher agency to enhance the learning of their students. Framing the debate alongside the issue of teacher autonomy, Simpson describes results from a two-year study, which brings together information from interviews, surveys, document analysis and digital stories from Australia, Canada, the UK and the US to assess the role of children’s literature in pre-service teacher education. Through cross-cultural comparison, this research captures the different levels of connection between politics, education systems, higher education and pre-service teachers. It exposes how politics, narrow views of professionalism and program structures in teacher education may adversely affect the development of pre-service teachers. This book presents a strong case that reading and responding critically to literary texts leads to better educational outcomes than basic decoding and low-level comprehension training. As such, this book will be of great interest to researchers and scholars working in the areas of teacher education and literacy and primary education. It should also be essential reading for teacher educators and policymakers.

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)0.000
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: none
Teacher disagreement score0.662
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.037
GPT teacher head0.285
Teacher spread0.248 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicLiteracy, Media, and EducationFrench-language works237,207