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Record W1971376745 · doi:10.1044/1058-0360(2003/081)

Single-Word and Conversational Measures of Word-Finding Proficiency

2003· article· en· W1971376745 on OpenAlexaff
Susan J. Tingley, Christiane S. Kyte, Carla J. Johnson, Joseph H. Beitchman

Bibliographic record

VenueAmerican Journal of Speech-Language Pathology · 2003
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsWord (group theory)PsychologyFluencyTask (project management)Rapid automatized namingTest (biology)Verbal fluency testWord AssociationAssociation (psychology)Cognitive psychologyLinguisticsCognitionPhonological awarenessLiteracyNeuropsychology

Abstract

fetched live from OpenAlex

Two studies with young adults as participants evaluated the relationship, presumed in the word-finding literature to exist, between slow, inaccurate performances in single-word-naming and semantic-retrieval tasks and disruptions to conversational fluency. The measures evaluated were the frequency of conversational disruptions and the scores from 3 single-word tasks: total time from the Rapid Automatized Naming task (RAN; M. B. Denckla and R. G. Rudel, 1976), standard score from the Brief Test of the Test of Adolescent/Adult Word Finding (TAWF; D. J. German, 1990), and total unique words from the Controlled Oral Word Association task (FAS; A. L. Benton and K. Hamsher, 1978). RAN time was the only significant predictor of the frequency of conversational disruptions, although this relationship was weak (R(2) =.11). In addition, single-word performances did not discriminate between groups of participants with differing levels of conversational fluency. Clinicians are cautioned against identifying word-finding deficits using single-word measures alone. Moreover, the theoretical construct of word-finding difficulties requires additional validation.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.293
Teacher spread0.266 · 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 designOther design
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

Citations16
Published2003
Admission routes1
Has abstractyes

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