Airport Screening, Surveillance, and Social Sorting: Canadian Responses to 9/11 in Context
Bibliographic record
Abstract
Since 9/11, aviation security has become a major preoccupation of Western governments, not least Canada's. Some unprecedented security measures have been taken, and all air travelers are aware both of how these now affect their need for certain documents and of the extra time required for air travel. When placed in a broader frame, however, these developments may be seen as rational expansions of existing measures increasingly common to what might be seen as the symbiotically growing "surveillance society" and "safety state." Here, surveillance has become a feature not of specific monitoring of suspects but of generalized social sorting of populations, in this case in relation to their perceived levels of dangerousness. And safety is the new criterion of good policy within risk-management regimes. The result, in Canadian airports, is a new emphasis on Advanced Passenger Information (API) and the Passenger Name Record (PNR) as the means of tracking travellers and the development of a coordinated plan under the new Canadian Air Transport Security Authority (CATSA) for the screening of passengers and baggage. The demands of global free trade mean that mobility of goods and persons is a high priority, but this is constrained by the need to demonstrate that airport conditions are safe and that certain classes of person do not cross the (internal) border easily.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.038 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".