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Record W4313324957 · doi:10.31893/multirev.2022013

Terrorism violence: assessing the impact of education programs through mass media on the adoption of security measures in Burkina Faso

2022· article· en· W4313324957 on OpenAlexaff
Marie Madeleine Ouoba, Emmanuel Lompo

Bibliographic record

VenueMultidisciplinary Reviews · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsTerrorismContext (archaeology)Mass mediaPolitical sciencePublic relationsDeveloping countrySample (material)PsychologyBusinessEconomic growthGeographyEconomicsLaw

Abstract

fetched live from OpenAlex

This paper investigates the impact of mass media education programs (MEP) on the adoption of security measures in the context of terrorism in Burkina Faso. Using a questionnaire and guided interviews, we collected information from a sample of 412 individuals living in Ouagadougou, Burkina Faso. The results suggest that caution was the most important security measure learned by the participants through MEP (39,6%), followed by collaboration with the army (30%) and reporting suspected behaviors to the authorities (20.8%). More importantly, individuals who were educated on terrorism through MEP display higher intentions to adopt security measures and tend to have positive attitudes toward the role of media in fighting terrorism violence. This finding clearly shows that for developing countries and all countries facing terrorism and lacking adequate counter-response tools, such as Burkina Faso, MEP should be an integral part of the system of public awareness to succeed in effectively countering this global threat.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.084
GPT teacher head0.414
Teacher spread0.330 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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