Inclusion and Diversity:Communities and Practices Across the World
Bibliographic record
Abstract
This volume presents a comprehensive overview of inclusion and diversity in education across the globe. It examines how more inclusive education systems can be built, and covers areas and topics such as disability studies, sexual minorities, and indigenous communities, marginalized communities among others. The book presents perspectives of experienced and distinguished experts and researchers on inclusive practices related to participation, equity, and access from countries such as India, USA, Australia, UK, Canada, South Africa, Japan, Pakistan, Rome, Hungary, Sweden, and others. It discusses how spoken language, race, gender, and religion contribute to inclusion and marginalization. The volume also explores ideas on how schools and educational systems can respond to diversity-related issues, and the lessons learnt about how to improve capacity for further inclusion. Additionally, it provides a holistic understanding of the classroom practices and interventions adopted to handle problems of students with diverse needs. This incisive and comprehensive volume will be of interest to students, teachers and researchers of education, inclusion and diversity, equity and access, disability studies, educational psychology, social work, sociology, and anthropology. It will also be useful for teacher educators of B.Ed. and B. El. Ed courses, and anyone who is associated with or working in the field of diversity and inclusion.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.002 | 0.002 |
| 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".