MétaCan
Menu
Back to cohort
Record W2207457306 · doi:10.1503/jpn.150317

Neurolaw: recognizing opportunities and challenges for psychiatry

2015· editorial· en· W2207457306 on OpenAlexvenueno aff
Gerben Meynen

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2015
Typeeditorial
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsnot available
Fundersnot available
KeywordsChild and adolescent psychiatryPsychiatryPsychologyMedicineOrbitofrontal cortexCognition

Abstract

fetched live from OpenAlex

A 40-year-old schoolteacher begins secretly collecting child pornography and making advances toward his young stepdaughter.[1][1] After evaluation by a psychiatrist and neurologist, an MRI is obtained, and it shows a huge orbitofrontal tumour. As soon as it has been resected, the legally relevant

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)0.004
metaresearch head score (Gemma)0.024
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.019
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0030.004
Scholarly communication0.0080.008
Open science0.0030.002
Research integrity0.0190.027
Insufficient payload (model declined to judge)0.0070.007

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 head0.173
GPT teacher head0.323
Teacher spread0.150 · 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
GenreEditorial

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

Citations34
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Psychiatry and NeuroscienceSame topicMental Health and PsychiatryFrench-language works237,207