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Record W2585968287

Big Brother’s shadow: Decline in reported use of electronic surveillance by Canadian Federal Law Enforcement

2013· article· en· W2585968287 on OpenAlexaboutno aff
Nicholas Koutros, Julien Demers

Bibliographic record

VenueeYLS (Yale Law School) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Criminal Justice and Data Protection
Canadian institutionsnot available
Fundersnot available
KeywordsLaw enforcementGovernment (linguistics)Shadow (psychology)LawLegislationPoliticsEnforcementPolitical scienceElectronic surveillanceFederal electionPublic administration
DOInot available

Abstract

fetched live from OpenAlex

Despite popular perception of increased government surveillance, particularly since 9/11, a longitudinal study of the Annual Reports on the Use of Electronic Surveillance, published by Public Safety Canada between 1973 and 2011, demonstrates the opposite trend. This article first outlines this decline to situate the use of electronic surveillance by federal law enforcement. The second section of the article advances legal, political, and practical influences which are likely contributing to the diminished use of wiretapping by police. The purpose of this article is to present quantitative evidence to better inform the ongoing debate around extending “lawful access” regimes in Canada. By using official government statistics as a foundation, this article provides a practical grounding to the theoretical academic and legal research that often informs law, legislation and public policy governing the use of surveillance technology.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.426

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.008
Science and technology studies0.0040.003
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.276
Teacher spread0.242 · 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 designObservational
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

Citations5
Published2013
Admission routes1
Has abstractyes

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