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Record W183229132 · doi:10.1177/215416470203700206

Applications of Measures of Speed of Mental Operations among Children with Intellectual Deficiency

2002· article· en· W183229132 on OpenAlexaff
Michel Loranger, Marie Claude Blais, Sandra L. Hopps, Michel Pépin, Jean-Marie Boisvert, Martin Doyon

Bibliographic record

VenueEducation and training in mental retardation and developmental disabilities · 2002
Typearticle
Languageen
FieldMedicine
TopicTechnology and Human Factors in Education and Health
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPsychologyMental deficiencyDevelopmental psychologyIntellectual disabilityMathematics educationCognitive psychologyPsychiatry

Abstract

fetched live from OpenAlex

This study attempts to verify the value of new measures of speed of mental operations among children with intellectual deficiency. Results on this collection of simple problem-solving tasks are examined in relation to those obtained on different traditional measures of cognitive skills, as well as a scale of adaptive behavior. The participants are 62 children aged 3 to 13 years old, whose mean score on the Stanford-Binet is 55.03 (SD = 12.34). The results show medium to high correlations between scores on these five computerized tasks and all other cognitive measures, as well as the adaptive behavior scale. The relevance of taking speed of response into account in the assessment of cognitive skills is discussed along with its implications for the intellectual assessment of special populations.

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)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.298
Teacher spread0.256 · 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

Citations3
Published2002
Admission routes1
Has abstractyes

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