International Practices in Special Education: Debates and Challenges
Bibliographic record
Abstract
Margret A. Winzer and Kas Mazurek combine two disciplines in this collection, comparative and international studies and special education, to explore the ways that diverse nations respond to persons who are exceptional. Their learned contributors also explore the changing parameters of special education, employing comparative studies theories and methods to document, explore, discuss, and analyze social and educational inclusion. International Practices in Special Education: Debates and Challenges travels the world to examine the progress of special education, from inclusive reform in Canada, for all in the United Kingdom, the reform-restructure-renew movement in Poland to the journey from awareness to action in the United States. Chapters describe the challenges and opportunities in the United Arab Emirates; conflicts regarding educational welfare in South Korea; new perspectives on special needs and inclusive education in Japan; facing inclusion in India; making the invisibles visible in Pakistan; problems and prospects in Nigeria; special needs education in Ethiopia; and the developments, prospects, and demands of special education in a rising China. One step forward, two steps backward describes Israel's special education issues. Germany's special education receives an international perspective; and education policy and pedagogy for students with disabilities in Australia, completes the analyses in this remarkable, comprehensive work of scholarship.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".