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Record W4306253083 · doi:10.1002/mhw.33409

New Jersey advances bill that would keep people with MI out of court system

2022· article· en· W4306253083 on OpenAlexaboutno aff

Bibliographic record

VenueMental Health Weekly · 2022
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsnot available
Fundersnot available
KeywordsCommitMental healthQuarter (Canadian coin)PrisonLawRecidivismState (computer science)Political scienceCriminologyPsychologyPsychiatryHistory

Abstract

fetched live from OpenAlex

New Jersey legislators advanced a bill last month that would divert nonviolent criminal offenders from the court system into community‐based mental health treatment, the New Jersey Monitor reported on Sept. 30. Supporters say more than one quarter of people now incarcerated have mental health disorders. Connecting them with medical treatment instead of jailing them would save the state money while also reducing recidivism by better addressing their needs, said Adam Sagot, a psychiatrist with Hackensack Meridian Health. “We are in an epidemic of mental health crises,” said Sagot, testifying in support of the bill before the New Jersey state Assembly's judiciary committee. “Anything we can do right now to start providing treatment to those individuals on as broad a scope as possible is in everyone's best interest,” he said. Assemblyman Robert Auth (R‐Bergen) said the state should first “test the waters” in a few counties to gauge whether such a concept would work before mandating it statewide. He also said he worried about not confining people “who may be borderline not quite there … and some other innocent victim gets killed or molested or whatever.” But Assemblyman Raj Mukherji (D‐Hudson), who chairs the committee and sponsored the bill with Assemblywoman Annette Quijano (D‐Union), said only people with diagnosed or suspected mental illnesses who commit nonviolent disorderly persons offenses or third‐ or fourth‐degree crimes would be referred for diversion.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.357
Teacher spread0.321 · 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 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
Published2022
Admission routes1
Has abstractyes

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