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Record W4396829708 · doi:10.1080/1070289x.2024.2353466

Islamophobia as an affective field: death and elimination

2024· article· en· W4396829708 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueIdentities · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsToronto Metropolitan UniversityMcMaster UniversityTrent University
Fundersnot available
KeywordsIslamophobiaField (mathematics)PsychologyPolitical scienceMathematicsPoliticsLaw

Abstract

fetched live from OpenAlex

Twenty young adults attended an arts-based workshop in Toronto, Canada and responded to the question of ‘what does Islamophobia feel like?’ in order to document their lived reality of everyday Islamophobia. The article focuses on the overarching ‘story of feelings’ marking the art-pieces in an effort to understand how the art-makers use emotional fields to make Islamophobia legible from their lived standpoint. The Muslim protagonist is consistently illustrated by the artmakers as occupying an affective field marked by an everyday spatial politics of contact-and-recoil, i.e. an emotional field of disgust, while made to endure a continuous depletion-centred onslaught of violence. This article addresses a subset of the illustrations using image-based deep-story methods to argue that ‘disgust’ is the primary racialized emotional field through which young Muslims conceptualize the operational life of Islamophobia.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.358
GPT teacher head0.638
Teacher spread0.280 · 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