Bibliographic record
Abstract
Debates about what constitutes quality in initial teacher education have resulted in a series of quality conundrums that have to be unravelled by teacher educators. Using the lens of scale and adopting a new approach to understanding quality, this book draws upon empirical research into five large-scale, high-quality university-based teacher education providers in Australia, Canada, England, New Zealand and the US. The resulting model of initial teacher education practice shows how ideological concepts and accountability structures around teacher education are in constant tension with operational realities. The book explores how successful large-scale providers have reconciled those tensions and conundrums to ensure their provision is consistently high quality. The accounts also present a robust defence for university-based teacher education. The practice-based accounts of how tensions around quality and scale are being reconciled reveal the competing discourses around teacher professionalism, research and the role of the university in teacher education. The analysis presented promises to change the way we view high-quality teacher education across all providers and international contexts, not just those of large scale. This book will be of great interest to teacher educators, policymakers and educational leaders.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".