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Record W2339063690 · doi:10.15664/jtr.1211

#IS_Fangirl: Exploring a New Role for Women in Terrorism

2016· article· en· W2339063690 on OpenAlexaff
Laura Huey, Eric Witmer

Bibliographic record

VenueJournal of Terrorism Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsWestern University
Fundersnot available
KeywordsRadicalizationCLARITYTerrorismKey (lock)Focus (optics)Social mediaSociologyGirlConceptual frameworkPolitical sciencePsychologySocial scienceComputer scienceComputer securityLawDevelopmental psychology

Abstract

fetched live from OpenAlex

In this paper we present initial results from an ongoing study of women affiliated with pro-IS networks on Twitter and other social media. Our particular focus is on 20 accounts belonging to individual identified as ‘fan girls.’ Drawing on an analysis of Twitter posts from these 20 accounts, we identify key characteristics of the fan girl in an attempt to bring conceptual clarity to this role and enhance our understanding of who these girls are and their potential for radicalization.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.006
Scholarly communication0.0060.006
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.233
GPT teacher head0.455
Teacher spread0.221 · 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 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

Citations31
Published2016
Admission routes1
Has abstractyes

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