Bibliographic record
Abstract
Good teacher education not only enhances the understanding and skills of new teachers, but increases the likelihood of them staying in the profession. In Priorities in Teacher Education, Clare Kosnik and Clive Beck argue that teacher preparation should be given sharper focus, identifying seven priority areas: program planning pupil assessment classroom organization and community inclusive education subject content and pedagogy professional identity a vision for teaching Long-time teacher education instructors and researchers themselves, the authors identified these priorities through literature-based research and the findings of a three-year study following twenty-two graduates through their first years of teaching. Packed with examples and quotes about these experiences, the book is broken down into seven chapters, each focusing on one of the seven priorities and containing a case study of one teacher whose experiences embody the priority being discussed. As the chapters progress, the authors increasingly demonstrate the interplay between the seven priorities, showing that none of them can be pursued in isolation, and building a comprehensive base of essential knowledge for beginning teachers. Teacher educators will find Priorities in Teacher Education a key guide to pre-service preparation, while new and student teachers will benefit enormously from reading the ‘front line’ accounts of their contemporaries.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".