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Record W1498147840 · doi:10.1108/jfc-11-2014-0056

Towards a common identity? The harmonisation of identity theft laws

2015· article· en· W1498147840 on OpenAlexaboutno aff
Jonathan Clough

Bibliographic record

VenueJournal of Financial Crime · 2015
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)OriginalityContext (archaeology)Identity theftLawMeaning (existential)Law enforcementPolitical scienceInternational lawValue (mathematics)SociologyCriminologyComputer securityPsychologyCreativityComputer science

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to consider potential criminal law responses to the global challenge of “identity crime”. In particular, it focuses on a specific offence of “identity theft”. It begins with a discussion of the meaning of “identity” in the context of modern transactions, before defining “identity crime” and related terms. Legal responses are then considered before turning to the importance of harmonisation. The transnational nature of modern identity crimes makes it essential that law enforcement agencies not only have suitable offences at their disposal but also the frameworks to facilitate international cooperation. Design/methodology/approach – Given the increasingly transnational and organized nature of modern identity crime, this paper adopts a comparative approach. It draws upon provisions from Australia, Canada, the UK and the USA. It also looks at responses to identity crime at the regional and international level. Findings – Although there is currently no international instrument which specifically and comprehensively addresses identity theft, it is argued that there is an urgent need for further international discussion as to the desirability and form of identity theft provisions. While international agreement may not be reached, such discussions are important in assisting countries to develop appropriate legal frameworks and capacity to address the modern fraud environment. Originality/value – It is hoped that this paper will contribute to, and facilitate, important ongoing discussions as to the most effective ways in which to tackle identity crime at the national and international levels.

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 categoriesnone
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 score0.708
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.057
GPT teacher head0.318
Teacher spread0.260 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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