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Record W1977521393 · doi:10.1353/aad.2012.0135

Deaf Children Creating Written Texts: Contributions of American Sign Language and Signed Forms of English

2000· article· en· W1977521393 on OpenAlexaff
Connie Mayer, C. Tane Akamatsu

Bibliographic record

VenueAmerican annals of the deaf · 2000
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsYork University
Fundersnot available
KeywordsSign languageAmerican Sign LanguageManual communicationLinguisticsBridging (networking)ComprehensionManually coded languageSign (mathematics)Meaning (existential)Language interpretationPsychologySociolinguistics of sign languagesReading comprehensionComputer scienceReading (process)Philosophy

Abstract

fetched live from OpenAlex

The objectives of this descriptive study were to investigate the ways in which American Sign Language (ASL) and English-based sign allow for comprehension of text content, and to determine how these two avenues of communication might mediate the process of reconstructing "signed meaning" in a written text. The authors argue that comprehensible input in a visual mode is possible in either ASL or English-based sign. They further claim that English-based signing may be an effective means of bridging the gap between inner speech and written text.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.014
GPT teacher head0.325
Teacher spread0.311 · 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

Citations40
Published2000
Admission routes1
Has abstractyes

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