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Record W4353021600 · doi:10.1093/schbul/sbac211

Language and Psychosis: Tightening the Association

2023· article· en· W4353021600 on OpenAlexafffund
Eric J. Tan, Iris E. Sommer, Lena Palaniyappan

Bibliographic record

VenueSchizophrenia Bulletin · 2023
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteWestern University
FundersCanadian Medical AssociationJanssen CanadaUniversity of OxfordFonds de Recherche du Québec - SantéSunovion
KeywordsPsychosisAssociation (psychology)PsychologyPsychiatryCognitive psychologyPsychotherapist

Abstract

fetched live from OpenAlex

This special issue of DISCOURSE in Psychosis focuses on the role of language in psychosis, including the relationships between formal thought disorder and conceptual disorganization, with speech and language markers and the neural mechanisms underlying these features in psychosis. It also covers the application of computational techniques in the study of language in psychosis, as well as the potential for using speech and language data for digital phenotyping in psychiatry.

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)0.009
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.004
Science and technology studies0.0020.003
Scholarly communication0.0060.009
Open science0.0010.011
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0210.002

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 head0.013
GPT teacher head0.281
Teacher spread0.268 · 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 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

Citations8
Published2023
Admission routes2
Has abstractyes

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