Interpreting crises through narratives: the construction of a COVID-19 policy narrative by Canada’s political parties
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.026 | 0.036 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".