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Record W2518895009 · doi:10.1007/s11873-016-0292-8

Le research domain criteria (RDoC), le réductionnisme et la psychiatrie clinique

2016· article· fr· W2518895009 on OpenAlexaff
Luc Faucher, Simon Goyer

Bibliographic record

VenueRevue de Synthèse · 2016
Typearticle
Languagefr
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsResearch Domain CriteriaHumanitiesPhilosophyPsychologyPsychiatryMental health

Abstract

fetched live from OpenAlex

The focus of the advocates of the Research Domain Critria (RDoC) on faulty brain circuits has led some to suspect it of being a reductionist enterprise. And because RDoC will eventually impact clinical psychiatry, some have feared that it will transform clinical psychiatry in a mindless and applied neurobehavioral science. We argue that if RDoC is officially endorsing a kind of reductionism, the particular kind of reductionism it endorses is not suffering from the shortcomings of more classical forms of reductionism. Because of that, at least in principle, RDoC could enrich rather than impoverish clinical psychiatry. This paper raises few potential problems of the RDoC for clinical psychiatry caused by its implicit epistemological reductionism.

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)0.049
metaresearch head score (Gemma)0.063
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.262

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.063
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0040.060
Scholarly communication0.0090.009
Open science0.0020.007
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.073
GPT teacher head0.400
Teacher spread0.327 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations2
Published2016
Admission routes1
Has abstractyes

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