Biopower under a state of exception: stories of dying and grieving alone during COVID-19 emergency measures
Bibliographic record
Abstract
During the COVID-19 pandemic, restrictions for visitors and caregivers in healthcare settings and long-term care (LTC) facilities were enacted in the larger context of public health policies that included physical distancing and shelter-in-place orders. Older persons residing in LTC facilities constituted over half of the mortality statistics across Canada during the first wave of the COVID-19 pandemic. Using the poststructuralist work of Agamben, Foucault and Mbembe we conducted a thematic analysis on news reports. The extracts of media stories presented in our paper suggest that the scholarship on (bio)power and necropolitics is central for understanding the ways the COVID-19 crisis reveals the pragmatic priorities-and the 'health' and political values-that undergird the moral imagination of the public, including the educated classes of advanced Western democracies. Our critical analysis shows that by isolating individuals who were sick, fragile, and biologically and socially vulnerable, undifferentiated population management policies like social distancing, when piled on the structural weakness of health systems, reproduced inequities and risk for those in need of medical care, advocacy, and social companionship in acute moments of illness, death and grief. Considering the unprecedented deployment of governmental power via public health interventions based on social regulation to protect the population during the crisis-how can we understand so much death and suffering among the most vulnerable?
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.031 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 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".