Islam on Campus
Bibliographic record
Abstract
Abstract This book explores how Islam is represented, perceived and lived within higher education in Britain. It is a book about the changing nature of university life, and the place of religion within it. Even while many universities maintain ambiguous or affirming orientations to religious institutions for reasons to do with history and ethos, much western scholarship has presumed higher education to be a strongly secularizing force. This framing has resulted in religion often being marginalized or ignored as a cultural irrelevance by the university sector. However, recent times have seen higher education increasingly drawn into political discourses that problematize religion in general, and Islam in particular, as an object of risk. Using the largest data set yet collected in the UK (2015–18) this book explores university life and the ways in which ideas about Islam and Muslim identities are produced, experienced, perceived, appropriated, and objectified. We ask what role universities and Muslim higher education institutions play in the production, reinforcement and contestation of emerging narratives about religious difference. This is a culturally nuanced treatment of universities as sites of knowledge production, and contexts for the negotiation of perspectives on culture and religion among an emerging generation. We demonstrate the urgent need to release Islam from its official role as the othered, the feared. When universities achieve this we will be able to help students of all affiliations and of none to be citizens of the campus in preparation for being citizens of the world.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.013 |
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".