A Critical Take on Opinion 1/15: Is the Glass Half Full or Half Empty?
Bibliographic record
Abstract
The 9/11 attacks tilted the scale of state actions against terrorism in the Western states towards an international cooperation. In this context, sharing of information related to terrorism in a timely manner with partner states has come to the forefront of the fight against terrorism. One such source of information is that relating to people's travels and its sharing is exacerbated by states ‘concerns regarding their own citizens who have gone to fight in a conflict zone abroad such as Syria. Widely known as Passenger Name Records (PNR), this particular type of information has been collected by air carriers for their own business purposes since the 1960s. Starting with the United States of America (USA), many states including the European Union (EU) have adopted laws in the last decade allowing their public authorities to request PNR data from airlines. On the one hand, these laws are supported on the ground that the use of the data is an effective measure to counter international terrorism. On the other hand, they entail grave human rights concerns because they allow the retention and use of a vast amount of information about individuals, irrespective of the fact that no criminal suspicion has fallen upon them. Furthermore, with the help of technological advancements, PNR data can be aggregated with the information contained in other databases to produce extensive profiles of individuals. These concerns came to the centre of attention with the Court of Justice of the European Union (CJEU)'s 2017 decision (Opinion 1/15 delivered on 26 July 2017), striking down a draft international agreement to be concluded between the EU and Canada on the transfer of PNR data due to its shortcomings in complying with the human right to privacy afforded under EU law (in particular the EU Charter of Fundamental Rights (Charter)). this contribution discusses this Opinion in relation to its implications for travel surveillance and protection against indiscriminate data transfer for anti-terrorism purposes. It argues that the CJEU's observations profoundly influence more privacy-friendly data collection instruments, but at the same time they raise questions on the intersection between human rights and the use of PNR data for travel surveillance in the interest of anti-terrorism purposes.
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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 | 0.025 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.097 | 0.069 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".