‘Even in death, we’re being denied our place as human beings’: Geographic Islamophobia and Muslim Cemeteries in the English-Speaking West
Bibliographic record
Abstract
Cemeteries are powerful spatial manifestations of belonging and integration for many marginalized communities, including Muslims in the west. This article examines attempts between 2007 and 2020 to establish Muslim cemeteries in four white, English-speaking, Christian-majority (WEC) countries, and the resulting backlash as a geographic form of Islamophobia. These countries are England, Scotland, Australia, and the United States. By drawing theoretically on the geographies of Muslim minorities, Islamic necrogeographies, and theories of Islamophobia and whiteness, we engage five case studies to provide a detailed examination of the Islamophobic objections to attempts to establish Muslim cemeteries. More specifically, we analyse the discursive strategies contained in the speech of hostile locals as presented in newspaper articles. Our analysis identifies a number of key themes mobilized by cemetery opponents to frame the burial sites as a threat to suburban or rural space, often in environmental terms. This preliminary transnational analysis seeks to begin a discussion of conflicts surrounding Muslim cemeteries and the geographic manifestations of Islamophobia in the context of normative WEC space in the rural and suburban English-speaking west.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".