Omar Khadr, Hannah Arendt, and the Racialization of Rights’ Discourse
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.060 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".