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Record W2405728190 · doi:10.1037/pas0000341

Low reliability of sighted-normed verbal assessment scores when administered to children with visual impairments.

2016· article· en· W2405728190 on OpenAlexafffund
Valerie S. Morash, Amanda McKerracher

Bibliographic record

VenuePsychological Assessment · 2016
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsVancouver Island University
FundersSocial Sciences and Humanities Research Council of CanadaInstitute of Education SciencesUniversity of California Berkeley
KeywordsPsychologyNonverbal communicationPsycINFODevelopmental psychologyTest (biology)Test validityVocabularyIntelligence quotientPsychometricsVerbal reasoningReliability (semiconductor)Standardized testPsychological testingClinical psychologyCognitionMEDLINEPsychiatry

Abstract

fetched live from OpenAlex

The most common and advocated assessment approach when a child cannot access visual materials is to use the verbal subscales of a test the psychologist already has and is familiar with. However, previous research indicates that children with visual impairments experience atypical verbal development. This raises the question of whether verbal subscale scores retain their reliability and interpretation validity when given to children with visual impairments. To answer this question, we administered a vocabulary subscale from a common intelligence test along with several nonverbal subscales to 15 early-blind adolescents (onset of ≤2 years). Reliability of only the vocabulary test scores was insufficient for high-stakes testing. This finding points to the broader issue of difficulties in assessing populations of exceptional children who experience atypical development trajectories, possibly making their assessment with common tests inappropriate. (PsycINFO Database Record

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.031
GPT teacher head0.383
Teacher spread0.352 · 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.

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

Citations15
Published2016
Admission routes2
Has abstractyes

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