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Record W3087975636 · doi:10.22230/cjc.2020v45n3a3585

The Anatomy of a National Crisis: The Canadian Federal Government’s Response to the 2015 Kurdi Refugee Case

2020· article· en· W3087975636 on OpenAlexaffvenueabout
Sara Siddiqi, Duncan Koerber

Bibliographic record

VenueCanadian Journal of Communication · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsBrock UniversityToronto Metropolitan University
Fundersnot available
KeywordsBlameImmigrationRefugeeCitizenshipPolitical scienceGovernment (linguistics)Prime ministerRefugee crisisPoliticsImmigration policyPublic administrationFederal electionPolitical economyLawSociologyMedicine

Abstract

fetched live from OpenAlex

Background A photograph of Alan Kurdi, a Syrian boy found dead on a Turkish beach, sparked a major Canadian political crisis. During a federal election campaign, Prime Minister Stephen Harper and Minister of Citizenship and Immigration Chris Alexander responded quickly to the incorrect implication that the government’s immigration policies caused the boy’s death. Analysis This article analyzes the appropriateness of Harper’s and Alexander’s response strategies during the hours right after the crisis broke. Conclusion and implications This article argues that the politicians faced an unusual challenge because, although the government’s policies had not actually caused the crisis, government leaders had to respond as if they had. Harper and Alexander mostly followed best practices but shifted the blame to the greater refugee crisis, which came across as disingenuous.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score0.957

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0770.020
Scholarly communication0.0120.003
Open science0.0030.008
Research integrity0.0090.013
Insufficient payload (model declined to judge)0.0100.001

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 head0.034
GPT teacher head0.338
Teacher spread0.305 · 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 designQualitative
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
Published2020
Admission routes3
Has abstractyes

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