Reform of teacher education in the Asia-Pacific in the new millennium : trends and challenges
Bibliographic record
Abstract
Preface Part I Introduction Reform of Teacher Education Amid Paradigm Shift in School Education Y.C. Cheng, K.W. Chow and Magdalena M.C. Mok Part II Trends of Teacher Education Reform The Contending Models of and Debate in Teacher Education in the United States David G. Imig Canadian Teacher Education in Transformation F. Michael Connelly and D. Jean Clandinin Current Trends in Canadian Teacher Education: The Ontario Experience Clare Kosnik and Clive Beck Patterns of Development of Chinese Teacher Education in a Reform Context Ying Jie Wang Malaysian Teacher Education into the New Century Molly N.N. Lee Reform of Teacher Education in India: Trends and Challenges Kiran Walia Part III Challenges in Teacher Education in the 21st Century The Quest for Professional Teaching Standards: The NBPTS Model Richard Beach Challenges and Prospects of Teacher Education Colleges and Institutions in Japan Masahiro Arimoto Korean National Universities of Education: Reform and Further Reform Hye Sook Kim Incorporating ICT in Practicum: An Australian Experience Geoff Rogers An American Cluster Placement Model for Enriching Field Experience Connie Titone, Robert Cunningham Professional Development of School Principals for Revitalizing Schooling in Malaysia Ibrahim Ahmad Bajunid Institutions of Teacher Education in Asia: Changes and Challenges Yin Cheong Cheng and King Wai Chow
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".