Tracing Terrorists: The EU-Canada Agreement
Bibliographic record
Abstract
Enhancing border security in support of the global “war against terrorism ” is very much en vogue these days, in particular as regards the control of air passengers. Seven years after 9/11, this trend is yet unbowed. While the build-up of defences occurs in most cases at the one-sided expense of civil liberties, the EU- Canada agreement of 2005 is different: quite justly it holds the reputation of a well-balanced instrument respecting the interests of citizens. Still- instead of serving as a model for future instruments- the agreement rather runs the risk of being scrapped at the next possible occasion. A close look at the “PNR mainstream”, as embodied by the EU- US branch of transatlantic relations with its four agreements rapidly succeeding between 2004 and 2008, reveals the opposite tendency away from data protection and towards an unconditional tightening of controls. The paper undertakes to closely examine the doubtful benefits of such approach by looking at the price to pay inter alia for “false positive ” mismatches and other collateral damages, while in turn the actual achievement of a higher degree of public security remains very much in the dark, most of all due to the impossibility of reaching a 100 % tightness of borders. As a result, no stringent reason emerges why one should take leave from the good practices established by the EU-Canada instrument.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".