Bibliographic record
Abstract
Literacy Teacher Educators: Preparing Teachers for a Changing World brings together the perspectives of 26 literacy/English teacher educators from four countries: Canada, U.S., UK, and Australia. In this unique text the contributors, of whom many are renowned experts in critical literacy and multiliteracies, provide readers with an overview of trends in literacy/English teacher education. The chapters begin with authors’ personal stories and current research, giving readers insight into the personal and professional worlds of the contributors. Included in each chapter is a rich description of approaches to literacy instruction in teacher education. These exemplary teacher educators show in concrete detail how they are addressing our evolving understanding of literacy . This timely text, written in a highly engaging style, will be of value to teacher educators throughout the world. I have never read anything quite like this book. It contains explicit representations of the conceptual frames and work of distinguished literacy teacher educators at various stages in their careers, accounts that provide a strong counter-narrative to the mainstream discourse in policy and education, that fully embrace the uncertainties and complexities of practice." From the Forward by Susan L. Lytle, Professor Emerita of Education in the Graduate School of Education, University of Pennsylvania
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.016 |
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".