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Record W4388990966 · doi:10.21810/jicw.v6i2.5419

Gender and Far Right Extremism

2023· article· en· W4388990966 on OpenAlexaffvenue
Leyde Rodriguez Guervos

Bibliographic record

VenueThe Journal of Intelligence Conflict and Warfare · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFar rightFraming (construction)PhenomenonIdeologyViolent extremismPoliticsGender studiesPsychologySocial psychologyPolitical scienceCriminologySociologyTerrorismLawHistory

Abstract

fetched live from OpenAlex

In the media and within the literature, far right extremism has been noted as being hypermasculine in character. Yet, the framing of these movements as being male dominated, though accurate, obscures the roles that women play within these spaces. This study furthers the academic understanding of the phenomenon by presenting a comprehensive examination of the role of gender within far right extremism as a political ideology and within far right extremist groups. Gendered differences are found in the actual support for these movements and in the motivating psychological factors behind such support. Gendered differences are also present in the roles that men and women perform within these groups, with women functioning as violent actors, thinkers, facilitators, promoters, activists, and exemplars, but being unable to access leadership positions within far right extremist groups.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.072
GPT teacher head0.350
Teacher spread0.278 · 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 designTheoretical or conceptual
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 routes2
Has abstractyes

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