“Consideration of Trade-offs Regarding COVID-19 Containment Measures in the United States: Implications for Canada,” by Mayvis Rebeira and Eric Nauenberg
Bibliographic record
Abstract
Rebeira and Nauenberg’s paper presents an exploratory analysis of a challenging question which I paraphrase as: “Were the extraordinarily costly social responses to the COVID-19 pandemic economically justified?” They wade bravely into the always controversial topic of assessing whether what governments spend to achieve health gain – or in this case prevent its loss – are worth it, in economic terms. They apply well-known methods of modelling incremental cost-effectiveness analysis for value of life years gained, balancing that with comparisons to value of statistical life years measures used in different sectors. They encounter and detail many uncertainties in assembling the evidence on the effects and costs of social restrictions to prevent COVID-19 infection and spread, and the economic support programs used to buffer the negative effects of the pandemic. Their conclusion, perhaps not surprisingly, is for the United States – maybe – and for Canada, with more apparent success in epidemic control, perhaps a bit better. Perhaps the greater value of their paper is not its conclusions, but rather its posing of the questions. Here are some things it led me to ponder: [continued in PDF / HTML]
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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 | 0.015 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".