MétaCan
Menu
Back to cohort
Record W4224265251 · doi:10.1080/19460171.2022.2067070

Interpreting crises through narratives: the construction of a COVID-19 policy narrative by Canada’s political parties

2022· article· en· W4224265251 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCritical Policy Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsNarrativeBiopowerPoliticsParliamentConstruct (python library)Political scienceNarrative inquirySociologyPopulationPolitical economyPublic administrationLawLiterature

Abstract

fetched live from OpenAlex

As an unprecedent global crisis, the COVID-19 pandemic required policy actors to make sense of the event while simultaneously constructing an effective policy response. In this article, we focus on the onset of the crisis in Canada and ask: how was a crisis narrative constructed and to what extent did the features of the emergent narrative vary across political elites? We bring together the Narrative Policy Framework (NPF) with Foucault’s ‘biopolitics of population’ to explain the construction of an initial crisis narrative that is consistent with the economic rationale of neoliberal governmentalities. Using an original collection of 1,331 Hansard statements from Canadian Members of Parliament during the first wave (March to June 2020), we employ inductive content analysis to assess elements of narrative form. This article contributes to broader work seeking to understand how various actors construct narratives around the crisis and the consequences of such narrativization for policy responses.

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.

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)0.001
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.005
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 head0.083
GPT teacher head0.454
Teacher spread0.371 · 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