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Record W4396686706 · doi:10.29173/mlj1239

Chapter 2 – A Social Network Analysis of the Toronto 18

2021· article· en· W4396686706 on OpenAlexaffabout
David C. Hofmann

Bibliographic record

VenueManitoba Law Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsRadicalizationSocial network analysisIdeologyTerrorismPeriod (music)SociologyCriminologySocial scienceMedia studiesPoliticsPolitical scienceSocial capitalLaw

Abstract

fetched live from OpenAlex

This chapter employs social network analysis in order to empirically explore the communication network established by the Toronto 18 in the three years before their arrest. It provides a basic conceptual overview of the extent, breadth, and nature of ideological and operational communiques between the disparate members within the Toronto 18 to further stimulate scholarly inquiry into similar relational dynamics within analogous terrorist groups. This chapter also provides readers with an understanding of certain group, social, and structural characteristics across three distinct periods: (1) the radicalization phase (January 2003 to October 2005); (2) the winter training camp (November 2005 to December 2005); and (3) the three-month period surrounding the Opasatika property buying trip (January 2006 to March 2006). Research results are then presented and discussed, along with a brief overview of areas for future research.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.715

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.001

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 head0.030
GPT teacher head0.308
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 designObservational
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

Citations1
Published2021
Admission routes2
Has abstractyes

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