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Record W2139752339 · doi:10.1177/0734282915573723

Implications for Educational Classification and Psychological Diagnoses Using the Wechsler Adult Intelligence Scale–Fourth Edition With Canadian Versus American Norms

2015· article· en· W2139752339 on OpenAlexaffabout
Allyson G. Harrison, Alana Holmes, Robert Silvestri, Irene T. Armstrong

Bibliographic record

VenueJournal of Psychoeducational Assessment · 2015
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsCambrian CollegeQueen's University
Fundersnot available
KeywordsPsychologyWechsler Adult Intelligence ScaleLearning disabilityClinical psychologyIntelligence quotientRaw scoreScale (ratio)PopulationWechsler Intelligence Scale for ChildrenDevelopmental psychologyIntellectual disabilitySet (abstract data type)PsychiatryRaw dataCognitionDemography

Abstract

fetched live from OpenAlex

Building on a recent work of Harrison, Armstrong, Harrison, Iverson and Lange which suggested that Wechsler Adult Intelligence Scale–Fourth Edition (WAIS-IV) scores might systematically overestimate the severity of intellectual impairments if Canadian norms are used, the present study examined differences between Canadian and American derived WAIS-IV scores from 861 postsecondary students attending school across the province of Ontario, Canada. This broader data set confirmed a trend whereby individuals’ raw scores systematically produced lower standardized scores through the use of Canadian as opposed to American norms. The differences do not appear to be due to cultural, educational, or population differences, as participants acted as their own controls. The ramifications of utilizing the different norms were examined with regard to psychoeducational assessments and educational placement decisions particularly with respect to the diagnoses of Learning Disability and Intellectual Disability.

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 score0.462
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.224
GPT teacher head0.499
Teacher spread0.275 · 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

Citations10
Published2015
Admission routes2
Has abstractyes

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