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Record W4387670579 · doi:10.3390/educsci13101033

Communication, Language, and Modality in the Education of Deaf Students

2023· article· en· W4387670579 on OpenAlexaff
Connie C. Mayer, Beverly J. Trezek

Bibliographic record

VenueEducation Sciences · 2023
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsYork University
Fundersnot available
KeywordsConversationDeaf educationModality (human–computer interaction)Cognitive reframingContext (archaeology)Spoken languageLiteracyPsychologyFocus (optics)PopulationLinguisticsPedagogySign languageComputer scienceSociologyCommunicationSocial psychology

Abstract

fetched live from OpenAlex

In the history of deaf education, questions attending communication, language, and modality have generated much discussion, and even heated debate. This should not be surprising as these questions touch on a fundamental issue that is central to policy and practice in the field—how to provide early, ready, and meaningful linguistic access. While one point of agreement is that such access is vital for age-appropriate language and literacy development, there is less consensus on how this access should be realized. This focus has heightened consequences and significance in the current context in which auditory access to spoken language is possible for the majority of deaf children. With a goal of reframing the conversation, the focus of this article will be on making the critical distinctions between language and modality that can inform understandings as to how access can be best achieved for an increasingly diverse population of deaf children and their families.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.012
Scholarly communication0.0080.005
Open science0.0010.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.081
GPT teacher head0.503
Teacher spread0.422 · 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 designQualitative
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

Citations17
Published2023
Admission routes1
Has abstractyes

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