Bibliographic record
Abstract
This book analyzes how over the last two decades, immigration regimes in three primary refugee-receiving states in the Global North – Canada, Australia, and the United Kingdom – have engaged with allegations about witchcraft-driven violence made by asylum seekers coming from Anglophone countries across the African continent. The work intervenes at the nexus of anthropological, historical, legal, developmental, and human rights literatures to offer fresh insights into extrajudicial violence and global migration. Taking witchcraft-based asylum cases as its focal point, it argues that the recent dramatic expansion in claims to refugee protection under the ‘particular social group’ category of the 1951 UN Refugee Convention reflects immigration authorities’ increasing willingness to consider how legally recognizable persecution can derive from cultural practices and beliefs. Reflecting critically on such cases, it advances understandings of how witchcraft beliefs and practices have persisted as significant engines of violence in the contemporary world. It sheds light both on the limits of legal pluralism and cultural relativism in asylum adjudication and on how social scientific expertise contributes not simply to the flow of ideas, but also to the channelling of people across national, cultural, and epistemological boundaries. The book will be essential reading for students and researchers in legal anthropology, African studies, human rights, transnational history, migration and refugee law and policy, and the history and anthropology of witchcraft.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".