Response to Peter Berman’s Commentary on “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
We are grateful to Dr. Berman for raising important points when analyzing an economy-wide crisis like the COVID-19 pandemic. We agree with Dr. Berman that COVID-19 had different behavioural responses from different groups; thus the best that can be done is to estimate average effects. To understand the impact by different groups would necessitate information that in these circumstances was unavailable. Further, these groups could have behaved differently between the pre- and post-vaccine eras. Though average effects do not take into account the possible wide distribution of the effectiveness of the containment strategies in different groups, we think they provide a reasonable source of evidence in a crisis situation where data is often sparse and the situation is dynamic. In regard to Dr. Berman’s two other points, [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 | 0.012 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.056 | 0.075 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".