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Record W2159188135 · doi:10.1002/meet.1450440231

Tools of the trade: Drugs, law and mobile phones

2007· article· en· W2159188135 on OpenAlexaff
Rhonda McEwen

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2007
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMobile phoneInternet privacyLaw enforcementMobile technologyContext (archaeology)Perspective (graphical)EnforcementIdentity (music)Set (abstract data type)Action (physics)Social identity theoryBusinessPublic relationsMobile deviceSociologyComputer scienceSocial groupPolitical scienceWorld Wide WebLawTelecommunicationsSocial science

Abstract

fetched live from OpenAlex

Abstract As a specific form of social computing mobile phones are changing the way people use and perceive their social contexts both at work and at play. Observations of mobile phone use in urban settings suggest that this medium can facilitate existing social practices and extend our everyday activities to create a set of distinct social practices associated with this Information and Communication Technology. In particular mobile phones support users in the active production of identity, whether this identity is socially determined to be “normal” or “deviant”. Written from a Social Informatics perspective this paper examines the mobile phone as a contemporary controversial technology in the context of its use in illegal drug‐dealing and the law enforcement of those practices. The relationship between illegal drug‐dealing and law enforcement responses is critically analysed highlighting the way both groups utilise mobile phone technologies to achieve their divergent goals. The paper concludes by offering a perspective on the social nature of mobile communications and suggestions for further research and action.

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)0.001
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.009
GPT teacher head0.251
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 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

Citations3
Published2007
Admission routes1
Has abstractyes

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