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Record W1518727321

Forensic Science and Miscarriages of Justice: Some Lessons from Comparative Experience

2009· article· en· W1518727321 on OpenAlexaffabout
Kent Roach

Bibliographic record

VenueSSRN Electronic Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicAutopsy Techniques and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCoronerConvictionPolitical scienceForensic scienceFederal courtEconomic JusticeLawState (computer science)CriminologyMedicineSociologySuicide preventionPoison controlEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

This paper provides a critical assessment of the National Research Council’s (NRC) 2009 report in light of comparative experience in Australia, Canada, and the United Kingdom. It suggests that the NRC’s proposals for federal regulation of the forensic sciences are more appropriate for a unitary state than a federal system. The NRC report could have been strengthened by examining the British experience with a forensic regulator and a 2008 report on forensic pathology in the Canadian province of Ontario. The Ontario pathology report has already produced tangible reforms to the practice of forensic pathology within coroners’ systems while the NRC unrealistically calls for the abolition of all coroner systems. The dangers of superficial reforms are examined, including a Canadian example of published research being misapplied in a manner that contributed to a wrongful conviction. Finally, the NRC’s pessimistic conclusions about judicial exclusion of unreliable forensic science are contrasted with recent and more optimistic reform proposals in Canada and the United Kingdom.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.028
GPT teacher head0.363
Teacher spread0.335 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations6
Published2009
Admission routes2
Has abstractyes

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