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Record W2007484410 · doi:10.1177/082957350401900110

The Application of a WISC-III Short Form for Screening Gifted Elementary Students in Canada

2004· article· en· W2007484410 on OpenAlexaboutno aff
Betty A. Reiter

Bibliographic record

VenueCanadian Journal of School Psychology · 2004
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsnot available
Fundersnot available
KeywordsWechsler Intelligence Scale for ChildrenPsychologyWechsler Adult Intelligence ScaleTest (biology)Short FormsIntelligence quotientDevelopmental psychologyClinical psychologyPsychiatryCognition

Abstract

fetched live from OpenAlex

This investigation explored the accuracy of a short form of the Wechsler Intelligence Scale for Children-Third Edition (WISC-III} in predicting Full Scale IQ when administered as a separate test. The Dumont-Faro short form (i.e., Picture Completion, Information, Coding, Block Design, and Vocabulary; Dumont & Faro, 1993) was administered to 60 Canadian elementary students who were referred for gifted eligibility purposes. The remaining WISC-III standard subtests, coupled with two supplementary subtests, were administered immediately following the Dumont-Faro short form so that Full Scale IQ scores could be calculated. In order to determine the actual amount of time saved by using the short form, administration times were recorded for each WISC-III subtest. When administered as a separate test, the Dumont-Faro short form IQ scores correlated highly (.88) with the Full Scale IQ scores. Moreover, this instrument reduced the average administration time of the WISC-III standard battery by 55%. It was concluded that the Dumont-Faro short form is an effective screening instrument to use with elementary students who are referred for gifted eligibility purposes in Canada. This instrument can assist school psychologists in meeting increased service demands while still providing effective intellectual assessment.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.160
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.353
Teacher spread0.315 · 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 teacher head, 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

Citations2
Published2004
Admission routes1
Has abstractyes

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