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Record W4404332686 · doi:10.1080/00450618.2024.2419106

AAFS (Victorian branch) Symposium: Science and Medicine in the Courts—Learning from the wrongful conviction of Kathleen Folbigg

2024· article· en· W4404332686 on OpenAlex
Emma Cunliffe, Carola G. Vinuesa, Rhanee Rego, Mehera San Roque, Gary Edmond, Jeremy Gans, Mai Sato, Kate Burridge, Stephen Cordner

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAustralian Journal of Forensic Sciences · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConvictionLawPolitical sciencePsychologyCriminologyHistory

Abstract

fetched live from OpenAlex

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?

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.091
GPT teacher head0.432
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it