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Record W4253419455 · doi:10.18192/potentia.v10i0.4511

Anders Breivik

2019· article· en· W4253419455 on OpenAlexaffvenue
Alexandre Madore

Bibliographic record

VenuePotentia Journal of International Affairs · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTerrorismContext (archaeology)Isolation (microbiology)CriminologyPolitical scienceSocial isolationSociologyPsychologyLawGeographyPsychiatry

Abstract

fetched live from OpenAlex

This analysis considers the importance of general strain theory (GST) in understanding contemporary far-right movements and violence involving white heterosexual men. General strain theory describes how objective and subjective strains can contribute to antisocial behaviours including terrorism. The mass murder committed by Anders Breivik in July 2011 in Norway will be considered as an application of this theory to terrorism. The analysis remains relevant, as evidenced by the most recent 2019 New Zealand mosque terrorism incidents. It begins with an overview of Breivik’s turbulent childhood and adulthood, marked by isolation and failed business ventures. Next, an outline of the July 2011 Norway attacks provides further context. After providing a detailed exploration of these attacks, this analysis will consider general strain theory in relation to the situation outlined above and it will be argued that perceived subjective and objective strain contributed to Breivik’s actions. More specifically, the subjective strains he experienced included social isolation and poor parental relationships. Conversely, objective strains provide an analysis of how Anders Breivik and others like him perceive their privileged position as being strained by migration and increasingly liberal gender norms. This analysis concludes with suggesting a role for social work in deescalating far right movements in Western liberal democracies.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.998

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.285
Teacher spread0.277 · 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.

Study designQualitative
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 routes2
Has abstractyes

Explore more

Same venuePotentia Journal of International AffairsSame topicTerrorism, Counterterrorism, and Political ViolenceFrench-language works237,207