Innovation and Accountability in Teacher Education: Setting Directions for New Cultures in Teacher Education. By Claire Wyatt Smith and Lenore Adie (Editors), 2018. 340 pp.
Bibliographic record
Abstract
Wyatt-Smith and Adie’s book introduction makes a strong case that teacher quality is key to better results. Three major teacher education innovations are described in this book. Many international reviews of teacher education and reform attempts have been published in the last decade. This textbook encourages governments to evaluate schools and beginning teacher education applicants (in Australia, both at intake and in national assessment before completion). Thus, this book focuses on curriculum orientation consistency since teacher opinions on curriculum orientation affect curriculum decision-making, teaching methodologies, and strategies (Cheung & Ng, 2000). Teachers are crucial to encourage and help students through varied learning and teaching methods. In a fun learning environment, teachers can help kids acquire values like acceptance and respect (Mak et al., 2018). Wyatt-Smith and Adie emphasize ‘the complex ecologies of teacher education’ (p. 13) using chapter contributors from Scotland, Norway, South Africa, Hong Kong, Singapore, Canada, the USA, New Zealand, and Australia. Traditions and culture mediate global imperatives in teacher education. The book’s worldwide perspectives are motivated by the need to create a scholarly platform for critical teacher education concerns in the 21st century and stimulate fresh, evidence-based thinking.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| 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".