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Record W2991999863 · doi:10.7202/1065854ar

Nouvelles technologies et cyberharcèlement : l’exemple du swatting

2019· article· fr· W2991999863 on OpenAlexvenueno aff
Nicolas Estano

Bibliographic record

VenueCriminologie · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicStalking, Cyberstalking, and Harassment
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Cette contribution tentera d’établir un état des lieux d’un phénomène qui s’est développé au cours des cinq dernières années en Europe, mais qui existe depuis une quinzaine d’années aux États-Unis, notamment chez les utilisateurs de jeux en ligne, à savoir le swatting . Les faits survenus en décembre 2017 à Wichita, au Kansas (États-Unis), où un homme trouva la mort viendront illustrer les potentialités dramatiques de cette pratique, présentée généralement par les auteurs comme un simple canular téléphonique. Nous tenterons à travers cet article de définir ce qu’est le swatting , ce que cela recouvre, quelles sont ses implications en termes de sécurité, les risques et enjeux qui y sont liés ainsi que les types de conduite qui y sont associés.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.179
GPT teacher head0.375
Teacher spread0.196 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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