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Record W1488435194 · doi:10.24124/c677/2014597

Terrorism Made Simpler: A Framing Analysis of Three Canadian Newspapers, 2006-2013

2015· article· en· W1488435194 on OpenAlexaffvenueabout
Srdjan Vučetić, Janelle Malo, Valérie Ouellette

Bibliographic record

VenueCanadian Political Science Review · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTerrorismNewspaperFraming (construction)MainstreamGlobePolitical scienceMedia studiesMedia coverageNews mediaSociologyLawHistoryPsychology

Abstract

fetched live from OpenAlex

How do mainstream Canadian newspapers portray contemporary terrorism? The 9/11 terrorist attacks on the United States and the ensuing “war on terror” has deeply impacted media coverage of terrorism and terrorism-related events around the world. Canada is no exception and scholars have begun examining various aspects of terrorism coverage in the Canadian media system. Inspired by framing theory, the following study adds to this growing literature by developing a model for understanding and evaluating media coverage of terrorism according to “degrees of simplification.” The model is applied to a sample of articles drawn from three Canadian newspapers in two periods of time—June 2006-June 2007 and June 2012-June 2013. Three main findings are discussed. First, both The National Post and La Presse tended to present terrorism-related news and analysis using simpler frames than The Globe and Mail. Second, the coverage of domestic terrorism was far less simplistic than the coverage of international terrorism in all three newspapers. Third, while simplifying frames were more frequent in 2006-7 than in 2012-3, the study finds weak evidence that mainstream framing practices employed by the Canadian newspapers radically changed between these two time periods.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.056
GPT teacher head0.350
Teacher spread0.294 · 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

Citations2
Published2015
Admission routes3
Has abstractyes

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Same venueCanadian Political Science ReviewSame topicTerrorism, Counterterrorism, and Political ViolenceFrench-language works237,207