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Record W3130992362 · doi:10.1017/s0008423921000214

Out of an Abundance of Caution: COVID-19 and Health Risk Frames in Canadian News Media

2021· article· en· W3130992362 on OpenAlexaffabout
Rebecca Wallace, Andrea Lawlor, Erin Tolley

Bibliographic record

VenueCanadian Journal of Political Science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsThe King's UniversityWestern UniversityCarleton UniversityUniversity of Toronto
Fundersnot available
KeywordsFraming (construction)Coronavirus disease 2019 (COVID-19)PandemicMedia coveragePublic healthNarrativeEpidemiologyPoliticsNews mediaPolitical scienceSocial mediaPublic relationsMedia studiesSociologyHistoryMedicineInfectious disease (medical specialty)LawDisease

Abstract

fetched live from OpenAlex

Abstract Although Canada's first documented case of COVID-19 appeared in mid-January 2020, it was not until March that messaging about the need to contain the virus heightened. In this research note, we document the use of the media's construction of risk through framing in the early stages of the pandemic. We analyze three dimensions of the health risk narratives related to COVID-19 that dominated Canadians’ concerns about the virus. To capture these narratives, we examine print and online news coverage from two nationally distributed media sources. We assess these frames alongside epidemiological data and find there is a clear link between media coverage, epidemiological data and risk frames in the early stages of the pandemic. It appears that the media relied on health expertise and political sources to guide their coverage and was responsive to the public health data presented to Canadians.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.379
GPT teacher head0.493
Teacher spread0.114 · 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.

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

Citations8
Published2021
Admission routes2
Has abstractyes

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