Coroners' Interested Advocacy: Understanding Wrongful Accusations and Convictions
Bibliographic record
Abstract
The problems of forensic pathologists' court testimony leading to wrongful convictions in cases of infant death, especially where mothers are charged with the offence, and of this testimony possibly involving gross distortion of scientific findings arise, in part, through a systematic misunderstanding by the law, and by judges and jurors, of forensic pathologists', and especially coroners', attitude toward their professional obligations. The law takes forensic pathological and coronial testimony to be “disinterested” scientific fact advanced purely for its inherent value in assisting the truth-seeking element of the trial process, and thus highly reliable as the basis of the exercise of the most coercive powers of government. Those delivering the testimony understand their task as part of a broader, long-standing public health and safety mandate to “speak for the dead to protect the living.” This clash of discursive frameworks has undermined the adversarial element of these trials, not just on a contingent case-by-case basis but over the courses of extended campaigns against child abuse and of professional forensic pathological careers.
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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 | 0.032 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".