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Record W2030576225 · doi:10.1051/medsci/200824172

Sémantique et hémisphère droit

2008· article· fr· W2030576225 on OpenAlexaff
Karima Kahlaoui, Yves Joanette

Bibliographic record

Venuemédecine/sciences · 2008
Typearticle
Languagefr
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsConcretenessRight hemisphereNeuroimagingPsychologyLateralization of brain functionCognitive psychologyLateralityVisual fieldSemantics (computer science)Semantic memoryNeuroscienceCognitionComputer science

Abstract

fetched live from OpenAlex

Although language is a function traditionally attributed to the left hemisphere, experimental and clinical reports indicate that the right hemisphere may also have a capacity to process verbal information. Indeed, some attributes of words, including their concreteness, imageability and emotional component, have been shown to be associated with right-hemispheric processing capacities. In addition, studies on brain-damaged, split-brain patients and studies realized with neuroimaging techniques have also suggested that the right hemisphere has some linguistic capacities. The main objective of this article is to review specific contribution of right cerebral hemisphere to semantic processing from three complementary approaches: (1) divided visual-field experiments with healthy participants, (2) studies of patients with acquired lesions of both left and right hemispheres, and (3) neuroimaging studies.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.003

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 head0.073
GPT teacher head0.333
Teacher spread0.260 · 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
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

Citations3
Published2008
Admission routes1
Has abstractyes

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