Bibliographic record
Abstract
While the WHO, public health experts, and political leaders have referenced solidarity as an important part of our responses to COVID-19, I consider how we build solidarity during pandemics in order to improve the effectiveness of our responses. I use Prainsack and Buyx's definition of solidarity, which highlights three different tiers: (1) interpersonal solidarity, (2) group solidarity, and (3) institutional solidarity. Each tier of solidarity importantly depends on the actions and norms established at the lower tiers. Although empathy and solidarity are distinct moral concepts, I argue that the affective component of solidarity is important for motivating solidaristic action, and empathetic accounts of solidarity help us understand how we actually build solidarity from tier to tier. During pandemics, public health responses draw on different tiers of solidarity depending on the nature, scope, and timeline of the pandemic. Therefore, I analyze both COVID-19 and HIV/AIDS using this framework to learn lessons about how solidarity can more effectively contribute to our ongoing public health responses during pandemics. Whereas we used institutional solidarity during COVID-19 in a top-down approach to building solidarity that often overlooked interpersonal and group solidarity, we used those lower tiers during HIV/AIDS in a bottom-up approach because governments and public health institutions were initially unresponsive to the crisis. Thus, we need to ensure that we have a strong foundation of respect, trust, and so forth, on which to build solidarity from tier to tier and promote whichever tiers of solidarity are lacking during a given pandemic to improve our 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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.027 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".