Bibliographic record
Abstract
The separation between religion and the state is widely regarded as a central feature of modernization processes, but sociological research has tended to neglect the extent to which even ‘secular states’ continue to manage religion in such institutions as prisons, hospitals and military establishments. This article extends the understanding of the state's management of religion by focusing on responses to the growth of religious diversity among prisoners and chaplains. In particular, it analyses the integration of Muslim chaplains into the prison systems of Canada and England & Wales. It is based on research – conducted between 2010 and 2012 – that investigated the frameworks governing religion in these two prison systems. This research involved analysis of official policies and regulations as well as transcripts of telephone interviews with a small sample of Muslim chaplains in both jurisdictions. The main focus of the findings reported in this article is on the implications that each prison system's arrangements for chaplaincy have for the work of Muslim chaplains and for questions about religious freedom and equality. These questions are timely in the context of controversies currently surrounding the increasing size of the Muslim prison population in England & Wales and Canada and the need for prisons in both jurisdictions to strike a fair balance between the recognition of religious diversity and the imperatives of security and equality.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".