One House of Worship with Many Roofs: Imposing Architecture to Mediate Sunni, Alevi, and Gülenist Islam in Turkey
Bibliographic record
Abstract
In 2013, Turkish media announcements declared that a complex containing a mosque for Sunni prayer, a cemevi (ceremonial hall) for the Alevi Muslim minority, and a shared courtyard and kitchen was to be built in the Tuzluçayır district of Mamak, Ankara. Computer renderings displayed the relationship of the parts within the walled precinct as equal in scale and scope, but the clear power imbalance between government-supported Sunni mosques and marginalized Alevi cemevis across Turkey suggested that merging the two sites of Islamic practice might have assimilationist aims. Alevi groups, heterogeneously comprised of hereditary spiritual lineages with a variety of teachings and practices that typically eschew Sunni tenets, protested the assimilationist pressures of the ‘mosque-cemevi’ while it was being constructed. The impetus for the project came from Fethullah Gülen, the religious leader of the ‘Hizmet’ movement living in exile in the United States. Once in favour with the Adalet ve Kalkınma Party-ruled Turkish government, he was accused of terrorist involvement in 2014 and of instigating the failed coup attempt of July 2016. The mosque-cemevi was never completed. This article will examine how state-supported Sunni, Alevi, and Gülenist Muslim selves encountered the mosque-cemevi project, and its role in imposing intra-Islamic relations in the Turkish Republic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".