Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.014 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".