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Record W2605623245 · doi:10.23907/2015.030

The Medicolegal Issues Surrounding the Case of Steven Truscott -a Forensic Pathology Perspective

2015· article· en· W2605623245 on OpenAlexaffabout
Kona Williams, Christopher M. Milroy

Bibliographic record

VenueAcademic Forensic Pathology · 2015
Typearticle
Languageen
FieldMedicine
TopicAutopsy Techniques and Outcomes
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsConvictionForensic pathologyAutopsyForensic scienceManner of deathTime of deathMedicineGeneral surgeryPsychologyLawPathologyCause of deathMedical emergencyPolitical scienceDisease

Abstract

fetched live from OpenAlex

One of the most difficult and controversial issues for any forensic pathologist is the determination of time of death. One of the best known cases in Canadian history that exemplifies this was that of Steven Truscott, who was convicted of murdering his classmate in 1959. Estimation of the time of death was critical, and largely done by examination of stomach contents at autopsy. Expert opinions on time of death using stomach contents and gastric emptying were largely anecdotal; there was insufficient scientific literature to support such precise estimations. The use of such evidence was key in Truscott's conviction and represents one of the great miscarriages of justice in our system. The conviction was overturned in 2007. Over 50 years later, despite a large body of scientific research in both the clinical and forensic fields, estimating the time of death from gastric emptying times remains largely inaccurate.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0160.027
Scholarly communication0.0070.007
Open science0.0030.005
Research integrity0.0290.033
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.043
GPT teacher head0.362
Teacher spread0.319 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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