Disintegration in the Age of COVID-19: Biological Contamination, Social Danger, and the Search for Solidarity
Bibliographic record
Abstract
Like any disaster, COVID-19 laid waste to infrastructure and the ability for a community to do community. But, unlike a tornado or nuclear meltdown, COVID-19 laid waste to social infrastructure in unique ways that only a disease can do. On the one hand, a pandemic brings biological dangers that, in turn, make all individuals—loved ones, too—into potential threats of biological contamination. On the other hand, the efforts to contain disease present social dangers, as isolation and distancing threatens mundane and spectacular ritualized encounters and mask-wearing heighten our awareness of the biological risk. By exploring the link between disasters and disease, this paper leverages the lens of contamination, beginning first with the barriers it presents to making and remaking the self in everyday life. Constraints on ritualized encounters, both in terms of delimiting face-to-face interaction and in determining that some spaces have contaminative risks, reduces collective life to imagined communities or shifts to digitally mediated spaces. The former intensifies the sense of anomie people feel as their social world appears as though it were disintegrating while the latter presents severe neurobiological challenges to reproducing what face-to-face interaction habitually generates. Finally, these micro/meso-level processes are contextualized by considering how institutions, particularly polity but also science, manage collective risk and how their efficacy may either contribute to the erosion of solidarity or provide a sense of support in the face of anomic terror. Using the US to illustrate these processes, we are able to show how an inefficacious state response weakens the already tenuous connective tissue that holds a diffuse and diverse population together, while also exposing and intensifying existing political, economic, and cultural fissures, thereby further eroding existing solidarity and the capacity to rebuild post-pandemic cohesion.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.080 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.021 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".