Infected Judgment: Problematic Rush to Conventional Wisdom and Insurance Coverage Denial in a Pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic created not only a public health crisis but also an insurance coverage imbroglio, prompting near-immediate business interruption claims by policyholders impacted by government restrictions ordered in response to the pandemic. Insurers and their representatives "presponded" to the looming coverage claims by quickly moving to denigrate arguments for coverage, engaging in a pre-emptive strike that has largely worked to date, inducing too many courts to rush to judgment by declaring-as a matter of law-that policy terms such as "direct physical loss or damage" do not even arguably encompass the business shutdowns resulting from COVID-19. Our closer examination of the term and of other key coverage questions suggests that policyholders have a much stronger case than suggested by the initial-and often superficial and conclusory-conventional wisdom flowing from the first wave of judicial decisions. Only a few courts have analyzed the COVID coverage debate with the type of reflective care, judicial humility, and respect for the trial process one would hope to see. The "early returns" in these coverage wars have been analytically disappointing, creating risk of an unfortunate path dependency or cascade of cases excessively narrowing the meaning of key terms such as "loss" and "damage," and diminishing the quality of future coverage decisions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it