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Record W2131835265 · doi:10.1177/1461444810365306

Tools of the trade: Drugs, law and mobile phones in Canada

2010· article· en· W2131835265 on OpenAlexaffabout
Rhonda McEwen

Bibliographic record

VenueNew Media & Society · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMobile phoneInternet privacyConceptualizationLaw enforcementContext (archaeology)EnforcementMobile technologyPerspective (graphical)PhoneMobile deviceBusinessSociologyComputer scienceLawPolitical scienceTelecommunicationsWorld Wide Web

Abstract

fetched live from OpenAlex

Observations of mobile phone use suggest that this medium facilitates existing social practices when used as a tool within, and at times outside, socially determined definitions of ‘normal’ or ‘deviant’ behavior. Written from a social construction of technology perspective, this article examines the mobile phone as a contemporary technology in the context of its use in illegal drug-dealing and the law enforcement of those practices in Canada. The relationship between illegal drug-dealing and law enforcement responses is critically analyzed, highlighting the way groups representing both sides utilize mobile phone technologies to achieve their divergent goals. Existing constitutional guidelines employed by law enforcement to support the use of mobile and wireless technologies for surveillance are considered, particularly considering the notion of privacy. The article concludes by challenging assumptions that mobile phones are primarily personal artifacts, and instead describes the inherently social nature of mobile communications, thereby calling for a re-conceptualization of current ideology on privacy.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.253
Teacher spread0.237 · 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 designNot applicable
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
Published2010
Admission routes2
Has abstractyes

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