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Record W2894890461

Community-Focused Counter-Radicalization and Counter-Terrorism Projects: Experiences and Lessons Learned

2018· book· en· W2894890461 on OpenAlexaboutno aff
Kawser Ahmed, Patrick Belanger, Susan J. Szmania

Bibliographic record

VenueDigital Commons - CSUMB (California State University, Monterey Bay) · 2018
Typebook
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsRadicalizationCounter terrorismTerrorismOver-the-counterPolitical sciencePublic relationsCriminologySociologyLawMedicine
DOInot available

Abstract

fetched live from OpenAlex

Following the launch of the global war on terror, western nations commissioned multiple community focused projects aimed at preventing terrorism and countering violent extremism. With an understanding that a comprehensive approach entails both proactive counter-radicalization measures and rehabilitation initiatives, these community-based projects typically aim to build resiliency and enhance prevention capacity within specific communities. This book focuses on the perceptions and experiences of twenty-nine community-based counter-radicalization project leaders in eight western countries: the United Kingdom, the United States, Canada, Australia, Germany, Belgium, Scotland, and France. By closely examining these efforts across multiple national contexts and in diverse communities, this book examines the challenges and opportunities of community-focused projects as identified by such projects’ leaders. At the book’s heart are interviews about community engagement and experience from the people most closely attuned to this vital work. By highlighting the importance of listening to community members, the book offers a rare chance to directly hear community members’ ideas, frustrations, and hopes.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.724
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.002
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 head0.061
GPT teacher head0.282
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 teacher head, not a consensus.

Study designNot applicable
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

Citations6
Published2018
Admission routes1
Has abstractyes

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