Bibliographic record
Abstract
Antiterrorism intelligence sharing across national borders has been trumpeted as one of the most promising forms of networked global governance. By exchanging information across the world, government agencies can catch terrorists and other dangerous criminals. Yet this new form of global governance is also one of the most dangerous. Even at the domestic level, secrecy and national security imperatives have placed intelligence agencies largely beyond legal and democratic oversight. But at the global level, accountability is missing entirely. Global cooperation among national intelligence agencies is extraordinarily opaque. The nature of the international system compounds the problem: these actors do not operate within a robust institutional framework of liberal democracy and human rights. Safeguarding rights in the transnational realm when governments conspire to spy, detain, interrogate, and arrest is no easy matter. Privacy is one of the most critical liberal rights to come under pressure from transnational intelligence gathering. This Article explores the many ways in which transnational intelligence networks intrude upon privacy and considers some of the possible forms of legal redress. Part II lays bare the different types of transnational intelligence networks that exist today. Part III begins the analysis of the privacy problem by examining the national level, where, over the past forty years, a legal framework has been developed to promote the right to privacy in domestic intelligence gathering. Part IV turns to the privacy problem transnationally, when government agencies exchange intelligence across national borders. Part V invokes the cause celebre of Maher Arar, a Canadian national, to illustrate the disastrous consequences of privacy breaches in this networked world of intelligence gathering. Acting upon inaccurate and misleading intelligence provided by the Canadian government, the United States wrongfully deported Arar to Syria, where he was tortured and held captive by the Syrian Military Intelligence Service for nearly one year. Part VI begins the constructive project of redesigning transnational networks to defend the right to privacy, with the safeguards of European intelligence and police networks serving as inspiration for transnational networks more broadly. These European systems feature two types of privacy safeguards: multilateral standards, to which all network parties must adhere, and unilateral standards, applicable under the law of one network party and enforced against the others through the refusal to share intelligence with sub-standard parties. Moving to the global realm, this Article concludes that the multilateral avenue is more promising than the unilateral one. Multilateral standards require consensus on common privacy norms, and consensus will be difficult to achieve. Notwithstanding this hurdle, multilateral privacy standards are crucial, for they will both enable the cooperation necessary to fight serious transnational crime and provide for vigorous protection of basic liberal rights.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".