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Record W2794561166 · doi:10.1016/j.eurpsy.2018.03.005

Grey matter or social matters? Causal attributions in the era of biological psychiatry

2018· editorial· en· W2794561166 on OpenAlexaff
Peter Brugger, Ira Kurthen, Neda Rashidi‐Ranjbar, Bigna Lenggenhager

Bibliographic record

VenueEuropean Psychiatry · 2018
Typeeditorial
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsAttributionContent (measure theory)Action (physics)PsychologySocial psychologyMathematics

Abstract

fetched live from OpenAlex

Fig. 1.Correlation between the strength of an individual's amputation desire and the surface area of a circumscribed region in the inferior parietal lobe (depicted in B).C: Boxand-whisker plots show distributions of plausibility ratings for two types of causality arguably implied by this correlation, i.e. neural primacy (dark bars) or behavioural primacy (light bars).A and B reprinted, (with permission), from ref. [6].

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)0.007
metaresearch head score (Gemma)0.026
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 score0.017
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.002
Science and technology studies0.0020.006
Scholarly communication0.0060.004
Open science0.0030.001
Research integrity0.0170.022
Insufficient payload (model declined to judge)0.0070.003

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 head0.044
GPT teacher head0.303
Teacher spread0.259 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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