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Record W4408436578 · doi:10.5744/rhm.2025.2428

Risk Metaphors in Canadian COVID-19 Public Health Communication

2025· article· en· W4408436578 on OpenAlexaffabout
Philippa Spoel, Michelle Reid, Emily Cooke, Catherine Copley

Bibliographic record

VenueRhetoric of Health & Medicine · 2025
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsLaurentian University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Risk communicationPublic health2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Health communicationPandemicVirologyMedicineEnvironmental healthPolitical sciencePublic relationsNursingOutbreakPathology

Abstract

fetched live from OpenAlex

This paper investigates the multi-faceted and ambiguous metaphorical connotations of “risk” terminology in COVID-19 updates delivered by Canadian public health officers (PHOs) during the first year of the pandemic. Our study reveals diverse and conflicting configurations of risk as both a manageable and unmanageable entity, a personal possession and an external location, an attribute of people and of spaces and activities, and a spectrum of degrees that (dis)identified those at lower and higher levels of risk. We argue that this situated tangle of metaphorical meanings contributed to a broader Canadian politics of neoliberal-communitarian health governance which was premised simultaneously on the active citizen’s individual responsibility to manage risk for self and others and on the vulnerabilization of citizens designated “most at risk.” For the RHM field, our study suggests new ways of exploring the meanings and implications of “risk” language within diverse contexts of health and medical communication.

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)0.005
metaresearch head score (Gemma)0.012
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: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.720

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0150.029
Scholarly communication0.0100.005
Open science0.0010.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.060
GPT teacher head0.400
Teacher spread0.340 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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