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Record W4210638185 · doi:10.1386/ijia_00063_1

The Anecdotal Archive: Building Design, Oral History, and the Notion of an Alevi Place of Worship

2022· article· en· W4210638185 on OpenAlexaff
Angela Andersen

Bibliographic record

VenueInternational Journal of Islamic Architecture · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTurkey's Politics and Society
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsVernacularCeremonyWorshipArchitectureIslamExpression (computer science)Vernacular architectureSociologyOral historyVisual artsHistoryAestheticsArtAnthropologyLiteraturePolitical scienceLawArchaeologyComputer science

Abstract

fetched live from OpenAlex

Scholarly investigation of communities with oral teaching traditions and vernacular building designs must step beyond established research frameworks for Islamic religious architecture to challenge typological and document-based categorizations of monumental buildings. Alevism’s student-teacher based socio-religious structure and service-oriented approaches to community life and religious expression, both in Turkey and in other parts of the world, shape interpretations of ceremonial cemevis , the houses of the cem ceremony. Alevi discussions of this architecture must play a significant role in the scholarly analysis of these sites. In this article, I emphasize the role of oral history in architectural studies by conducting interviews with architects who have participated in design competitions and contributed to a dialogue on a nascent contemporary Alevi monumental idiom. The article also highlights the considerations that arise in the course of examining both traditional and modern Alevi spaces, and reflects on the role of engagement with Alevis when researching cemevis and ceremonial settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.018
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.016
GPT teacher head0.286
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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