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Record W4280590054 · doi:10.1080/14643154.2022.2076010

Conceptualizing the role of mediation in an online American Sign Language teaching model for parents of deaf children

2022· article· en· W4280590054 on OpenAlexaff
Kristin Snoddon, Krishna Madaparthi

Bibliographic record

VenueDeafness & Education International · 2022
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAmerican Sign LanguageMediationPsychologyMainstreamSign languageDeaf educationLanguage acquisitionPedagogyDevelopmental psychologyLinguisticsMathematics educationSociology

Abstract

fetched live from OpenAlex

This paper discusses the role of mediation as it arose in developing and teaching two online American Sign Language (ASL) courses for parents of deaf children during the COVID-19 pandemic. Deaf children and their families who are still acquiring ASL have ongoing learning needs that are most often not met in mainstream educational systems, and these inequities have deepened during the pandemic. Combining reception, production, and interaction, mediation is a mode of language activity in the Common European Framework of Reference for Languages (CEFR) that involves “languaging” to develop ideas and facilitate understanding and communication. In this nine-month study, intensive parent ASL courses were adapted and developed for rapid implementation of online instruction in order to meet the second or additional language ASL learning needs of parents of deaf children. Online questionnaire, interview, observational, and assessment data were gathered regarding participating parents’ learning processes and experiences. As study findings reveal, a main theme that arose was the role of mediation in terms of alleviating various barriers for participants and facilitating the linguistic and cultural dimensions of parents’ online ASL learning and understanding through cognitive and relational means.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.229

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 studies0.0000.000
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 head0.036
GPT teacher head0.394
Teacher spread0.358 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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