Alternatives to sharing COVID-19 data with law enforcement: Recommendations for stakeholders
Bibliographic record
Abstract
During the COVID-19 pandemic, in some jurisdictions, police have become involved in enforcing coronavirus-related measures. Relatedly, several North American jurisdictions have established COVID-19 data sharing protocols with law enforcement. Research across a range of fields has demonstrated that involving police in matters of public health disproportionately impacts the most vulnerable and does more harm than good. This is reflected in the consensus against COVID-19 criminalization that has emerged among civil society organizations focused on HIV, human rights, and harm reduction. The European Data Protection Board has also released guidelines against re-uses of COVID-19 data for law enforcement purposes. This article offers an overview of the harms of criminalizing illnesses and strategies for health stakeholders to seek alternatives to sharing COVID-19 data with police agencies while facilitating interoperability with healthcare first responders. It also presents case studies from two North American jurisdictions - Ontario and Minnesota - that have established routine COVID-19 data sharing with police. We recommended seven alternatives, including designating COVID-19 data as sensitive and implementing segmented interoperability with first responder agencies. These guidelines can help ensure that health information technology platforms do not become vehicles for the criminalization of COVID-19, and that health data stay within the health system.
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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 | 0.095 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.010 | 0.023 |
| Research integrity | 0.025 | 0.018 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".