AAFS (Victorian branch) Symposium: Science and Medicine in the Courts—Learning from the wrongful conviction of Kathleen Folbigg
Bibliographic record
Abstract
Kathleen Folbigg spent 20 years in jail, wrongly convicted for smothering her four infant children. An unprecedented second judicial Inquiry found they died naturally. New genetic and psychiatric evidence unlocked the wrongful conviction, but it was always questionable. A Symposium held by the AAFS (Victorian Branch) detailed the timeline of the numerous events, then identified and discussed the issues. What were the failings and why were they missed? What part did expert witnesses, judges and lawyers play in this wrongful conviction? Did the prosecution appeal to misogyny? How did the first Inquiry get it so wrong? How did it fail to correctly understand the genetics? Was Ms Folbigg treated disrespectfully, and if so, what did that mean about fact finding? How did the failures of disclosure affect the original conviction and its various appeals? What explains the NSW Court of Appeal’s decision to reject an appeal based on jury misbehaviour during Ms Folbigg’s trial? Should we be more careful about the language we use in criminal trials? There was general agreement that the frailties of Kathleen Folbigg’s convictions were readily visible from the beginning. Will the criminal justice system learn the lessons that emerge from this case?
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".