Transnational comparison of the impact of COVID-19 on medicolegal death investigations and the administration of justice: Early stages of the pandemic
Bibliographic record
Abstract
COVID-19 has had an unprecedented impact on arguably every sector of our criminal justice system. To assess the impact that this global health crisis has had on our medicolegal investigations and administration of justice during the early stages of the pandemic, this research aims to give voice to the lived experiences of medicolegal death investigators (coroners, medical examiners and pathologists). This research involved in-depth interviews and follow-ups with experienced personnel from Canada (3), Italy (1), the United Kingdom (1) and the United States (4). Results suggest that despite facing similar challenges, each individual office has had to develop their own strategies to overcome obstacles during the early stages of the pandemic. These results help identify overlapping areas for constructive policy and procedural changes, including recommendations for workflow adaptations, strategic partnerships and other approaches to best prepare for subsequent health crises.
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How this classification was reachedexpand
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".