Bibliographic record
Abstract
Literacy learning continues to be central to schooling, and is currently of major concern to educators, policy developers, and members of the public alike. However, the proliferation of communication channels in this digital era requires a fundamental re-thinking of the nature of literacy and the pedagogy of literacy teaching and teacher education. This text brings together papers by experts in teacher education, literacy, and information technology to help chart a way forward in this complex area. Because of their background in teacher education, the authors are realistic about what is appropriate and feasible â they do not just jump on a technology bandwagon â but they are also able to provide extended examples of how to embed technology in the practice of teacher education. âTaking a multi-disciplinary perspective (literacy, teacher education and digital technology) and informed by a range of empirical studies, policy analyses and scholarly reflection, this book makes a unique contribution to the literature on one of educationâs most pressing challenges: how we prepare teachers of literacy at a time when understandings of literacy are expanding. Chapters by leading researchers are complemented by those offering illuminating vignettes of practice that, in turn, provide opportunities for interrogation by the rich theoretical toolkit that characterizes the field. The book is thoughtfully structured and manages a coherence that is rare in edited collections. An impressive and heartening read.â â Viv Ellis, Professor of Education at Brunel University, England and Bergen University College in Norway.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.194 | 0.058 |
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".