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Record W2237729157

Protecting the Social Value of Privacy in the Context of State Investigations Using New Technologies

2007· article· en· W2237729157 on OpenAlexaff
Arthur J. Cockfield

Bibliographic record

VenueSSRN Electronic Journal · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicLaw, Rights, and Freedoms
Canadian institutionsQueen's University
Fundersnot available
KeywordsInformation privacyPrivacy by DesignPrivacy policyPersonally identifiable informationCharterRight to privacyInternet privacyContext (archaeology)State (computer science)The Right to PrivacyValue (mathematics)Privacy lawAgency (philosophy)Privacy laws of the United StatesDemocracyPolitical scienceLaw and economicsBusinessLawSociologyPoliticsComputer scienceHuman rights
DOInot available

Abstract

fetched live from OpenAlex

In pursuit of security, governments around the world are adopting powerful technologies to collect and share detailed personal information, potentially leading to an erosion of privacy. This article discusses how legal analysis should respond to situations where technology developments challenge privacy interests in the context of state investigations. In particular, judges, lawyers and policy-makers need to take into more explicit account both the individual rights aspect of privacy as well as the social value of privacy, that is, society's interest in preserving privacy apart from a particular individual's interest. Both of these aspects of privacy are critical to the functioning of our democratic state. This approach demonstrates that legal analysis sometimes overstates the tension between privacy and security as both can be portrayed as social interests. To establish that a state search is constitutionally permissible under s. 8 of the Charter of Rights and Freedoms, the need to protect the social value of privacy compels the Crown to prove that a state agency has developed and initiated reasonable policies to govern the collection, use and disclosure of personal information through new surveillance technologies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.318
Teacher spread0.282 · 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 teacher head, not a consensus.

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

Citations8
Published2007
Admission routes1
Has abstractyes

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