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Record W4214590541 · doi:10.1177/08944393211011584

The Human Factor of Cybercrime

2021· article· en· W4214590541 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSocial Science Computer Review · 2021
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCybercrimeAction (physics)Human traffickingCraftCollective actionSociologyPolitical scienceCriminologyData scienceComputer scienceThe InternetGeographyLawPoliticsWorld Wide Web

Abstract

fetched live from OpenAlex

This volume highlights the central role of the human factor in cybercrime and the need to develop a more interdisciplinary research agenda to understand better the constant evolution of online harms and craft more effective responses. The term “human factor” is understood very broadly and encompasses individual, institutional, and societal dimensions. It covers individual human behaviors and the social structures that enable collective action by groups and communities of various sizes, as well as the different types of institutional assemblages that shape societal responses. This volume is organized around three general themes whose complementary perspectives allow us to map the complex interplay between offenders, machines, and victims, moving beyond static typologies to offer a more dynamic analysis of the cybercrime ecology and its underlying behaviors. The contributions use quantitative and qualitative methodologies and bring together researchers from the United States, the United Kingdom, the Netherlands, Denmark, Australia, and Canada.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.041
GPT teacher head0.340
Teacher spread0.299 · 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