CASE 8: Case Attribution for COVID-19: Who Counts What?
Bibliographic record
Abstract
The COVID-19 pandemic has emerged as an important topic of discussion at the Public Health Agency of Canada’s federal, provincial, and territorial tables. Representatives from each Canadian province and territory have come together to discuss the discrepancies noted in the attribution of COVID-19 cases between jurisdictions. Senior epidemiologist, Nina Mendez, is leading a case attribution project to give provinces and territories a forum for discussing any jurisdiction issues they encounter when reporting COVID-19 cases. Nina notices discrepancies in the way provinces and territories are reporting cases, where the majority of jurisdictions have been reporting cases based on official permanent residence, however two jurisdictions have been attributing cases based on location of diagnosis. This discrepancy in attribution leads to a discussion about how different protocols influence the way public health measures are implemented within each jurisdiction. Specifically, in the context of when individuals such as students, commuters, visitors, or long-term temporary workers are away from their permanent residence long enough that the risk of disease transmission no longer applies to their permanent residence jurisdiction.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".