“Not Scientific” to Whom? Laypeople Misjudge Manner of Death Determinations as Scientific and Definitive
Bibliographic record
Abstract
When someone dies unexpectedly, a medical examiner may perform an autopsy to determine how they died (i.e., manner of death). Recent studies found that cognitive bias can affect manner of death judgments, such that extraneous non-medical information may cause the same death to be judged as either a homicide or accident, which has significant legal ramifications. In response, leading medical examiners clarified that manner of death is “not scientific” and “often does not fit well in court.” Yet medical examiners often testify in court, and little is known about how fact-finders appraise their judgments. To address this gap, we conducted two experiments in which mock jurors read and evaluated a medical examiner’s testimony at a murder trial (modeled after the real-world case of Melissa Lucio), while varying the expert’s opinion (i.e., homicide or accident) and the defendant’s attributes (i.e., an affluent white or underprivileged Latina woman). Overall, participants rated the medical examiner’s testimony as highly scientific, credible, and convincing, and it strongly affected their verdicts and belief in the defendant’s guilt, irrespective of the defendant’s attributes. Moreover, participants unexpectedly rated the expert as even more credible if they ruled the death a homicide rather than an accident. Our data thus reveal a worrisome disconnect between how medical examiners characterize their judgments (i.e., as nonscientific and tentative) and how jurors appraise those judgments (i.e., as highly scientific and practically dispositive). We discuss ways to remedy this disconnect, including reforming death investigation practices to curtail bias and encourage standardization and transparency.
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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 | 0.020 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".