World Customs Journal THE CASE FOR RISK-BASED AVIATION SECURITY POLICY
Bibliographic record
Abstract
In the wake of the 9/11 attacks, governments in the United States (US), Canada, and Europe implemented additional aviation security measures. Although the rhetoric of risk-assessment is often heard, actual policy was driven largely by political imperatives to reassure frightened populations that air travel was still safe. The challenge in dealing with terrorist threats is always one of deciding where to invest scarce resources to maximum benefit. This inevitably requires difficult choices. The premise of this paper is that risk assessment provides an essential framework for making such choices and should be applied more consistently to aviation security. The goal should be to wean legislators away from enacting mandates not based on risk analysis. Legislators should direct the national aviation security policymaker/regulator to address problems within some kinds of quantitative parameters. Details of making actual policy and resource-allocation decisions should be left to the aviation security agency. That agency, in turn, should be flexible in tailoring policies to changing threats and different situations at individual airports which vary enormously in type, size, and configuration. While it seems likely that commercial aviation will remain a high-profile potential target, spending billions every year on static defences at airports is almost certainly a poor use of resources. Whether any kind of effort can succeed in educating elected legislators and opinion leaders to these realities is the most difficult challenge. 1.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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".