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Record W1964804033 · doi:10.1080/07434610212331281271

Grammatical issues in graphic symbol communication

2002· article· en· W1964804033 on OpenAlexaff
Ann Sutton, Gloria Soto, Susan Blockberger

Bibliographic record

VenueAugmentative and Alternative Communication · 2002
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSymbol (formal)Augmentative and alternative communicationComputer scienceCommunicationGraphic communicationLinguisticsNatural language processingPsychologyMultimediaGraphic designProgramming language

Abstract

fetched live from OpenAlex

In this article, issues and concepts related to the study of production, comprehension, and acquisition of syntax and morphology by children who need augmentative and alternative communication (AAC) systems are reviewed. The use of graphic symbols when vocal speech is severely limited presents significant challenges to the typical process of language acquisition. A conceptual and theoretical context is presented, and concerns that seem unique to AAC are explored. Productive lines of research are proposed to address language acquisition issues and to improve AAC system designs and intervention programs for language development in children who require AAC.

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)0.002
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.012
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.080
GPT teacher head0.383
Teacher spread0.303 · 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

Citations93
Published2002
Admission routes1
Has abstractyes

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