The Role of Coloniality, Decoloniality, and Education in Shaping Perspectives on Extremism
Bibliographic record
Abstract
This book extends a comprehensive overview of the treatment of extremism in education in Bangladesh, using a study of perceptions among students to explore proactive measures for the prevention of various types and forms of extremism prevalent among youth. It offers a critical, holistic, and student-centred study of the role of formal education in shaping perceptions of extremism and intersectional differences among individuals, drawing on data from university students. The author employs post-colonial theory and multicultural educational approaches to highlight how understandings of extremism differ across young adults and policymakers. Ultimately, it demonstrates that students’ overall understanding of extremism is much broader than that of policymakers, and how understandings differ between male and female students at the intersection of rural and urban locations and socio-economic positions. As such, it foregrounds a need to involve and organize formal education as a proactive means to raise awareness and counter all forms of extremism, through incorporating specific teaching strategies into pedagogical practices to foster an anti-communalist, humanistic, critical multicultural, and cosmopolitan outlook among students. It will appeal to scholars and researchers with interests across multicultural education, comparative and international education, the sociology of education, extremism, and conflict and peace studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".