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Record W4386442049 · doi:10.1080/1478601x.2023.2254097

Support for vigilantism in cyberspace: exploring procedural justice, distributive justice, and legal legitimacy

2023· article· en· W4386442049 on OpenAlexaff
Leanna Ireland

Bibliographic record

VenueCriminal Justice Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsMount Royal University
Fundersnot available
KeywordsLegitimacyCyberspaceProcedural justiceEconomic JusticeGovernment (linguistics)Political scienceCriminologyDistributive justiceCriminal justicePublic relationsDeviance (statistics)Law enforcementSociologyLawPsychologyPoliticsPerceptionThe Internet

Abstract

fetched live from OpenAlex

Vigilantism, harmful acts conducted in response to social deviance and criminal activities, are increasingly happening in cyberspace. These cyber acts often have detrimental effects, and efforts at prosecution attempts can be unsuccessful, difficult, or nearly impossible. An understanding of support for cyber vigilantism can help deter activity and mitigate some of the associated harms. The paper, using US-based survey data, tests whether public perceptions of distributive and procedural justice, via perceived legitimacy of the criminal justice system, are associated with support for cyber vigilantism. The findings provide support for the process-based model of legitimacy. Procedural justice is mediated by legitimacy in its influence on support. Distributive justice, however, has a strong direct association with cyber vigilantism support. The paper discusses the implications of the findings for the field of cyber vigilantism.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.005
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.230
GPT teacher head0.451
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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