Securitization versus sovereignty? Multi-level governance, scientific objectivation, and the discourses of the Canadian and American heads of state during the first wave of the COVID-19 pandemic
Bibliographic record
Abstract
The global health regime is caught in a paradox, whereby connecting “human” to “(inter)national” security to prevent the spread of infectious diseases unwittingly introduces into this complex and expertise-reliant domain of “low politics” the notion of “sovereign decisionism”—states’ prerogative to identify a threat and counter it with exceptional measures that may in turn constrain their ability to unilaterally securitize disease. This article introduces an analytical framework presenting three pathways through which state leaders with different conceptions of sovereignty and varying constraints on their legitimacy among their domestic audiences may nevertheless securitize policy domains traditionally considered as falling within the scope of sub-state “low politics.” Two of the pathways begin with scientific objectivation rather than politicization, and one trades power concentration for collaboration with sub-state and global authorities. I then compare the Canadian and American responses during the first wave of the coronavirus pandemic to uncover how these contextual factors disposed Donald Trump to politicize COVID-19, while Justin Trudeau emulated the World Health Organization's securitization of the virus without centralizing state powers.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.103 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| 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".