Risk Technologies and the Securitization of Post-9/11 Citizenship: The Case of National ID Cards in Canada.
Bibliographic record
Abstract
The attacks of 11 September 2001 on Washington and New York continue to influence how governments manage im/migration, citizenship, and national security. One of the more contentious national security responses to the events of 9/11 in Canada has been the drive to introduce a biometric national identification card. In this paper, we argue that the drive for a Canadian national ID card is bound up in ideological processes which threaten to exacerbate, rather than to alleviate, state insecurities pertaining to risk, citizenship, and border (in) security. We maintain that ‘proof of status’ surveillance technologies, such as biometrically-encoded ID cards, lead to the ‘securitization’ of citizenship, and we conclude that ID cards threaten to destabilize the modern spatializations of sovereignty that they are purported to uphold under the guise of national security.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.045 | 0.029 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".