Dialogical Breakdown and Covid-19: Solidarity and Disagreement in a Shared World
Bibliographic record
Abstract
This article considers the limitations, but also the insights, of Gadamerian hermeneutics for understanding and responding to the crisis precipitated by the Covid-19 pandemic. Our point of departure is the experience of deep disagreements amid the pandemic, and our primary example is ongoing debates in the United States about wearing masks. We argue that, during this dire situation, interpersonal mutual understanding is insufficient for resolving such bitter disputes. Rather, following Gadamer’s account of our dialogical experience with an artwork, we suggest that our encounter with the virus gives rise to new ways of seeing and experiencing ourselves and the world. Further, we draw on Gadamer’s account of the fusion of horizons to show how even competing perspectives on wearing masks arise within a shared space of meaning created by the virus. These insights provide hope for an improved model of political dialogue in the world of Covid-19.
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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 | 0.041 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.028 | 0.108 |
| Scholarly communication | 0.026 | 0.033 |
| Open science | 0.003 | 0.032 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".