Bibliographic record
Abstract
Teacher education programs work with prospective teachers that are destined to work in a wide variety of contexts, and thus must find ways to allow individuals to engage personally with what is offered while maintaining more general program-wide experiences and certification requirements. So what do teacher education institutions intend for their students to learn within the boundaries of their programs? This book is composed of answers to more specific questions that begin to explore the broader inquiry as to what Canada’s teachers should know. The 21 chapters that form this volume are divided according to their consideration of one of four focus questions examining a) the impact of globalization on the teacher capacities in Canada; b) how capacities are developed and influenced during and after teacher education programs; c) how teacher education programs measure capacities and are held accountable for the development of these capacities; and d) how these capacities may or may not serve the needs of a diverse student body. This book is evidence that there is no longer a one-model-fits-all mentality prevailing in Canadian teacher education, rather, teacher educators are embracing the context of their communities, provinces, and the world to provide critical conversations and experiences for their students.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".