"No Silver Bullet Solution": Cruel Optimism and Canada’s COVID-19 Public Health Messages
Bibliographic record
Abstract
Science twines through many of the discussions related to hope for a return to normalcy within public discussions of COVID‑19. The framings of techno-scientific solutions for COVID‑19 are similar to those that are presented to address many societal problems. The messy scientific and regulatory underpinnings of this desired silver bullet rarely make it fully into view. Technoscientific-related hope and its associated affects can operate as a kind of “cruel optimism” (Berlant 2010, 2011). It can be an affective response to return to life as “normal” that is psychologically soothing, even as its enactment may replicate destructive social, political, and economic structures. Hope and technoscience thread throughout the interactions between journalists and health officials in the health press briefings in the first wave of the COVID‑19 pandemic. Technoscientific complexity that challenges the desire to return to normal is rarely brought up in Ontario and Nova Scotia public health briefings. But when it is, health officials in this zone of interaction balance explanations of scientific reality and caution, while attempting to not crush hope for a techno-scientifically mediated return to normal. As such, public health discourse obscures or tempers cruel optimism rather than directly confronting it.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.041 | 0.035 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".