Medical Examiners’ Independence is Vital for the Health of the American Legal System
Bibliographic record
Abstract
Forensic pathologists play a vital role in the justice system in matters concerning questions of death. Accurate investigation, examination, reporting, and testimony by forensic pathologists are important to determine and demonstrate the cause and manner of death of individuals who die under sudden, unexpected, or violent circumstances. Cases involving political influence on the work of forensic pathologists have gained notoriety within the media and have been a source of concern for experts who practice in this highly selective field. In 2009, the National Research Council (NRC) of the National Academies published a report listing recommendations to strengthen the forensic sciences throughout the country. A specific recommendation within the report contends that medical examiner and coroner offices should be independent from, or at least autonomous within, law enforcement agencies and prosecutors’ offices. It is our position that forensic pathologists working in or for medical examiner or coroner offices or as private consultants should be permitted to objectively pursue and report the facts and their opinions of those cases which they are investigating independent of political influences from other agencies and institutions within their respective jurisdictions. This paper discusses three cases involving political influence, presents survey data of NAME members concerning such influences, and reviews the recommendations of the NRC.
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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 | 0.024 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".