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Record W1906224185 · doi:10.1108/jfc-06-2014-0030

A routine activity perspective on online victimisation

2015· article· en· W1906224185 on OpenAlexaboutno aff
Bradford W. Reyns

Bibliographic record

VenueJournal of Financial Crime · 2015
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
Fundersnot available
KeywordsPhishingLegal guardianHackerVictimisationPsychologyOriginalityPerspective (graphical)MalwareInternet privacyCybercrimeSocial psychologyComputer securityThe InternetComputer sciencePoison controlHuman factors and ergonomicsWorld Wide WebMedicinePolitical science

Abstract

fetched live from OpenAlex

Purpose – The purpose of this study is to test a comprehensive routine activity framework on three types of online victimization. Prior research has utilized routine activity theory to explain varied online forms of victimization, but most have focused on its person-based forms. The present study, therefore, expands upon this research to examine the effects of online exposure, online target suitability and online guardianship upon phishing, hacking and malware infection victimization. Design/methodology/approach – Secondary data from the 23rd Cycle of the Canadian GSS were used to address the study’s research questions using binary logistic regression analyses. Findings – Particular online behaviors were consistently and positively related to all three types of online victimization, including booking/making reservations, social networking and having one’s information posted online. Other online routines exhibited unique effects on online victimization risk. Originality/value – In support of the theory, the results suggest that online exposure and target suitability increase risks for phishing, hacking and malware victimization. Online guardianship was also positively related to victimization, a finding that runs counter to theoretical expectations.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.047
GPT teacher head0.351
Teacher spread0.304 · 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 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

Citations94
Published2015
Admission routes1
Has abstractyes

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