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Record W1536169057

Towards a Right to Privacy in Transnational Intelligence Networks

2007· article· en· W1536169057 on OpenAlexaboutno aff
Francesca Bignami

Bibliographic record

VenueMichigan Journal of International Law · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Criminal Justice and Data Protection
Canadian institutionsnot available
Fundersnot available
KeywordsDignityInformation privacyTerrorismPrivacy lawPolitical scienceConventionGovernment (linguistics)Right to privacyThe Right to PrivacyPrivacy policyAutonomyInternet privacyLawHuman rightsBusinessComputer science
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.023
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0110.041
Scholarly communication0.0160.027
Open science0.0020.013
Research integrity0.0110.015
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.027
GPT teacher head0.331
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

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