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Record W2746763331 · doi:10.1386/jucs.4.1-2.263_1

Sacred space: Muslim and Arab belonging at Ground Zero

2017· article· en· W2746763331 on OpenAlexaff
Huma Mohibullah

Bibliographic record

VenueJournal of Urban Cultural Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGround zeroOrientalismNarrativeWorld trade centerFundamentalismRhetoricIslamIslamophobiaCenter (category theory)PoliticsSpace (punctuation)Zero (linguistics)Common groundMedia studiesPolitical scienceSociologyHistoryLawArtLiteratureArchaeologyTheologyTerrorismPhilosophy

Abstract

fetched live from OpenAlex

Abstract The area surrounding New York’s World Trade Center was politicized immediately after the 9/11 attacks and named ‘Ground Zero’. This article discusses how orientalist tropes as well as narratives of ‘Islamic fundamentalism’ and ‘sacred space’ came to be embedded there. It uses two examples to examine how such spatialized politics have impacted Arab and Muslims New Yorkers: the Park51 community centre (popularized through media as ‘The Ground Zero Mosque’), and the lesserknown Little Syria district. It sheds light on Ground Zero’s significance for Arab and Muslim belonging in the United States – specifically, how Arab and Muslim claims to space around the World Trade Center subvert Islamophobic rhetoric that casts them as outsiders and enemies, and position them instead as fully American.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0090.018
Scholarly communication0.0050.002
Open science0.0000.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.065
GPT teacher head0.372
Teacher spread0.306 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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