The Use of Children's Literature in Teaching: A study of politics and professionalism within teacher education
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".