Framing, inequality and the politics of insecurity during the COVID-19 pandemic in Canada and in the United States
Bibliographic record
Abstract
This chapter looks at the COVID-19 pandemic through the lens of the politics of insecurity where potential collective threats are framed by political actors. The chapter also stresses the importance of focusing on economic and social inequalities when studying the politics of insecurity. As suggested, this exploration benefits from close attention to both inequalities and existing policy legacies. At the same time, the ideational aspect of the politics of insecurity, which frequently takes the form of framing (i.e. the strategic use of ‘symbols and concepts’ ( Campbell, 2004 , p. 94) to shape individual and collective perceptions), is crucial to grasp their subjective and intersubjective construction over time. This aspect of insecurity remains understudied and is the focal point of the present chapter. It begins with the discussion of a framework for the study of the politics of insecurity centred on the analysis of framing processes and the strategies of political actors related to them. Emphasizing the importance of agenda-setting and framing processes is a direct contribution of this chapter to the scholarship on security and insecurity as they interact with economic, social and territorial inequalities. It is through this framework that the chapter explores the politics of insecurity surrounding the COVID-19 pandemic. Although the chapter focuses primarily on Canada and the United States, global forces and the situation in other countries are also discussed. Empirically, using a qualitative comparative case study approach and drawing on academic, media and government sources to illustrate theoretical claims, the chapter primarily studies the discourse of domestic and international policymakers about COVID-19 as a source of collective and global insecurity that closely intersects with patterns of inequality.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.052 | 0.032 |
| Scholarly communication | 0.017 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".