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Record W2794731999

The Paradox of Source Credibility in Canadian and U.S. Domestic Counterterrorism Communications

2018· article· en· W2794731999 on OpenAlexaboutno aff
Patrick Belanger, Susan J. Szmania

Bibliographic record

VenueDigital Commons - CSUMB (California State University, Monterey Bay) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsnot available
Fundersnot available
KeywordsCredibilityNegotiationTerrorismGovernment (linguistics)Political scienceContext (archaeology)Deterrence theoryRhetorical questionDiplomacyCivil societyPublic relationsLawPublic administrationPolitical economySociologyPolitics
DOInot available

Abstract

fetched live from OpenAlex

This article examines the interface of rhetorical theories of credibility and the domestic counterterrorism communications of government and nongovernment actors in Canada and the United States. We track evolving attempts to controvert terrorists’ propaganda through official and unofficial channels. Each country has a comprehensive counterterrorism strategy that employs both deterrence and “soft” approaches, such as diplomacy and engagement. Our focus is the latter. First, we discuss how governments undertook counterterrorism communications following September 2001. Second, we explore attempts to engage credible voices outside of government, such as former violent extremists and religious leaders, in the fight against terrorism. We conclude that although counterterrorism messaging must negotiate the challenge of source credibility, further examination of elements such as context, audience reception, and digital engagement is needed to refine domestic campaigns launched by government and civil society actors.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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 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

Citations8
Published2018
Admission routes1
Has abstractyes

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