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Record W2773012205 · doi:10.13169/islastudj.4.1.0072

Can Muslims Fly? The No Fly List as a Tool of the “War on Terror”

2017· article· en· W2773012205 on OpenAlexaboutno aff
Uzma Jamil

Bibliographic record

VenueIslamophobia Studies Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsnot available
Fundersnot available
KeywordsIslamophobiaTerrorismSecuritizationPoliticsPolitical scienceOrientalismSuspectColonialismWar on terrorPolitical economyMedia studiesSociologyLawHistory

Abstract

fetched live from OpenAlex

The securitization phenomenon is based on a racialized logic that predates 9/11 and has roots in the discourse of Orientalism, the practices of European colonialism and, in the more recent times of the 20th century, the internment of Japanese Canadians during World War II. This article analyzes the “no fly list” as a counter-terrorism tool used by the Canadian government in the “war on terror.” It situates the analysis in the political context of the securitization of Muslims, the social and political processes that construct them as threats to the nation. It examines the evolution of the “no fly list” since 2001 and analyzes its impact on Muslims in Canada, drawing on well-publicized cases. It critiques the list's effectiveness based on the distinction between information and knowledge as tools to fight the “war on terror.” The “no fly list” contributes to Islamophobia through disproportionately profiling racialized Muslim and Muslim-looking passengers as members of a suspect community.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.825

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.019
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.367
Teacher spread0.322 · 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 designNot applicable
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

Citations6
Published2017
Admission routes1
Has abstractyes

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