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Record W2266499148

Invading the Mind: The Right to Privacy and the Definition of Terrorism in Canada

2007· article· en· W2266499148 on OpenAlexaboutno aff
Alysia Davies

Bibliographic record

VenueSSRN Electronic Journal · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Law and Evidence
Canadian institutionsnot available
Fundersnot available
KeywordsCharterTerrorismJurisprudencePolitical scienceLegislationLawRight to privacyOrder (exchange)Element (criminal law)Information privacyIdeologyEconomic JusticePoliticsLaw and economicsInternet privacySociologyBusinessComputer science
DOInot available

Abstract

fetched live from OpenAlex

This paper deals with the predictive crime model that was unleashed in the war against terrorism and its implications the right to privacy in Canada. The paper is divided into four parts. First, it examines the definition of terrorism found in the 2001 anti-terrorism legislation, which includes a motivational element that requires investigators to prove an offence was committed for a political, religious or ideological purpose, objective or cause. Then it discusses the development and philosophical underpinnings of the right to privacy and the current state of protection this right under the Canadian Charter of Rights and Freedoms, with a particular focus on the nascent privacy jurisprudence under section 7. The paper then lays out a hypothetical scenario based on the application of offences under the anti-terrorism legislation, and demonstrates how the interests that the right to privacy is supposed to protect could be violated by provisions that are based on the terrorism definition. Finally, the paper looks at the direction that Charter jurisprudence may need to take in order to protect privacy in the future, looking in particular at the principles of fundamental justice under section 7, their current content, and the need to adapt them to the new technological crime fighting environment.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.276
Teacher spread0.258 · 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.

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

Citations3
Published2007
Admission routes1
Has abstractyes

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