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Record W7115947994 · doi:10.18623/rvd.v22.n7.4057

REFORMING MALAYSIA’S DEATH-INVESTIGATION SYSTEM: THE APPOINTMENT OF CORONERS AND THE ESTABLISHMENT OF THE CORONER’S COURT

2025· article· W7115947994 on OpenAlexaboutno aff

Bibliographic record

VenueVeredas do Direito Direito Ambiental e Desenvolvimento Sustentável · 2025
Typearticle
Language
FieldMedicine
TopicAutopsy Techniques and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsCoronerLegislaturePreambleIndependence (probability theory)AccountabilityStatutory lawAuditSeparation of powers

Abstract

fetched live from OpenAlex

In Malaysia, Sessions Court judges perform coroner functions without dedicated training or institutional support, limiting the independence and effectiveness of death investigations. This article examines Malaysia’s coroner appointment framework and the absence of specialised Coroner’s Courts, contrasting it with the established systems in England and Wales, Australia, and Canada. Comparator jurisdictions appoint specialised coroners and operate purpose-built coronial courts with clear statutory mandates. The study identifies key structural gaps in Malaysia’s model and highlights the need for legislative reform, professionalised appointments, and independent coronial institutions to enhance accountability and public confidence. The reform proposals outlined, which include separating the coroner role from judicial duties, setting up a National Coroner’s Office, creating independent Coroner’s Courts, modernising legal frameworks, instituting comprehensive training, and implementing robust audit mechanisms, collectively aim to transform Malaysia’s coroner system into a world-class institution. These measures require legislative reform, institutional capacity building, dedicated funding, and political commitment despite possible resistance or initial costs.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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.

Study designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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