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Record W2551524644 · doi:10.1109/isi.2016.7745459

Discovering structure in Islamist postings using systemic nets

2016· article· en· W2551524644 on OpenAlex
Nasser A Alsadhan, David B. Skillicorn

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAuthorship Attribution and Profiling
Canadian institutionsQueen's University
Fundersnot available
KeywordsRepresentation (politics)Construct (python library)Computer scienceCluster analysisAnalyticsPoliticsSystemic functional linguisticsSystemic functional grammarContrast (vision)Natural language processingLinguisticsData scienceArtificial intelligencePolitical scienceLaw

Abstract

fetched live from OpenAlex

Textual analytics based on representations of documents as bags of words has been extremely successful. However, analysis that requires deeper insight into language, into author properties, or into the contexts in which documents were created requires a richer representation. Systemic nets are one such representation. The jihadist groups AQAP, ISIS, and the Taliban have all produced English magazines designed to influence Western sympathizers. Using a model of jihadi language, we construct a systemic functional net for these magazines, and contrast the structures revealed by clustering using words versus clustering using the choices implicit in systemic functional nets. We then show that the systemic functional net derived from the magazines is consistent with the structure present in two Islamist forums, and therefore reveals two different mindsets, one that is political and another that is religious, that seem widely held within the relevant communities.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

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

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 head0.022
GPT teacher head0.260
Teacher spread0.238 · 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

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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