MétaCan
Menu
Back to cohort
Record W2041930861 · doi:10.1080/13669870600924477

Public Perception of Terrorism Threats and Related Information Sources in Canada: Implications for the Management of Terrorism Risks

2006· article· en· W2041930861 on OpenAlexaffabout
Louise Lemyre, Michelle C. Turner, Jennifer E. C. Lee, Daniel Krewski

Bibliographic record

VenueJournal of Risk Research · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTerrorismPreparednessWorryGovernment (linguistics)PerceptionPopulationPolitical sciencePublic opinionRisk perceptionPublic relationsPsychologyPoliticsEnvironmental healthMedicineLaw

Abstract

fetched live from OpenAlex

A national survey of terrorism‐related risk perceptions was recently conducted in Canada, with a total of 1,502 adult Canadians interviewed by telephone. This paper provides a descriptive account of the perception of terrorism threats in Canada, specific types and effects of terrorism, as well as information sources on terrorism. Overall, respondents reported that terrorism was a low to moderate threat to the Canadian population and an even lower threat to themselves as individuals. They also indicated that they currently worry little about terrorism in Canada. The Canadian media was cited as the source most often referred to when seeking credible information about terrorism, whereas elected politicians and government officials were referred to the least. Demographic differences in perceptions of terrorism were examined, with gender representing an important determinant. Survey results are discussed in relation to their implications for addressing and managing the risks of terrorism as well as preparedness planning in Canada.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.032
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.137
GPT teacher head0.407
Teacher spread0.269 · 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

Citations58
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Risk ResearchSame topicRisk Perception and ManagementFrench-language works237,207