Conceptions of inclusion:widening ideas
Bibliographic record
Abstract
School inclusion is a perennially popular yet polemic topic in most countries. This timely book explores what is known about inclusion, highlighting outstanding examples of inclusion to provide a complete overview of successful inclusion.The book concentrates on how to make inclusion work – from the view of internationally established practitioners in the field of teacher education - with a focus on what variables are likely to make a difference in practice.What Works in Inclusion? covers three key aspects: Theories of inclusive education Examples of how inclusion can be encouraged and facilitated What prevents inclusion from being successfulDrawing on case studies from a wide range of countries, including USA, Australia, UK, Canada and Italy, there is focus on the positive aspects of inclusion: ‘how’ it can work and ‘what actually works’, helping you understand successful aspects of inclusion as well as developing an understanding of how inclusive education can best be implemented.In addition to the research-based accounts of how to make inclusion work, the book considers the difficulties that can arise in attempting to achieve successful inclusion and how such barriers can be overcome, to ensure a successful inclusive experience for both teachers and students. This is a key text for all serving and aspiring teachers and SENCOs, as well as those interested in inclusion and SEN in schools, and will inform and challenge in equal measure.Contributors: Adrian F Ashman, Robert Conway, Joanne Deppeler, Roberta Fadda, Laurel M. Garrick Duhaney, Fraser Lauchlan, Margo Mastropieri, Kim M. Michaud, Brahm Norwich, Petra Ponte, Diane Richler, Richard Rose, Spencer J. Salend, Tom Scruggs, Roger Slee, Jacqueline Thousand, Richard Villa, Catharine Whittaker“Focusing on both theory and practice, this timely volume provides a refreshing set of challenges to all of us who are committed to the development of more inclusive education systems. The presentation of ideas and experiences from different countries is particularly powerful in this respect.”Professor Mel Ainscow, University of Manchester, UK“Boyle and Topping provide a collection of salient chapters on critical issues pertaining to inclusive education from a collection of world leaders in the field. This book is scholarly, current, and research-based, yet at the same time readable and informative for a wide audience of university teachers and their students, along with practicing educators in the field. Recognizing that inclusive education is an ongoing project this book nevertheless provides a rigorous gestalt of inclusive education theory, practical advice for implementation, and potential barriers to success. This is one of the finest books on this topic currently available.”Professor Tim Loreman, Faculty of Education, Concordia University College of Alberta, Canada
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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 | 0.027 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.016 | 0.124 |
| Scholarly communication | 0.033 | 0.065 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".