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Record W2524188898 · doi:10.26522/ssj.v10i1.1127

Omar Khadr, Hannah Arendt, and the Racialization of Rights’ Discourse

2016· article· en· W2524188898 on OpenAlexaffvenueabout
Valentina Capurri

Bibliographic record

VenueStudies in Social Justice · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTorture, Ethics, and Law
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRacializationHuman rightsCitizenshipSubject (documents)SociologyDenialLawArgument (complex analysis)Political scienceGender studiesPoliticsPsychoanalysisPsychology

Abstract

fetched live from OpenAlex

In this paper, I focus on the story of Omar Khadr, a Canadian minor who was held captive in Guantanamo Bay for a decade, to demonstrate that, at times, neither citizenship nor human rights offer any protection to those who, like Khadr, are citizens of a country and are certainly human beings, yet have been deprived of the rights associated with those statuses. By drawing on Hannah Arendt’s argument in The Origins of Totalitarianism, as well as some of her subsequent work, I critically assess the debate regarding whether the rights conferred upon citizens are the only true barriers against abuse, or whether human rights have become a more effective protection. I suggest that this debate is sterile as it fails to recognize that the issue is not which set of rights offers a better guarantee of protection, but how the discourse around citizenship and human rights remains racialized, to the point where certain individuals are considered neither citizens nor humans, and therefore are potentially subject to abuse. Focusing on Canada’s treatment of Khadr, I argue that racialization is the root cause of his denial of rights. My analysis aims to contribute to existing literature by refocusing the “rights debate” to demonstrate that any discussion around abstract rights fails to address the experiences of those racialized subjects whose rights have been denied.

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 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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0000.000
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 head0.078
GPT teacher head0.431
Teacher spread0.353 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations7
Published2016
Admission routes3
Has abstractyes

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