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Record W4405108004 · doi:10.7202/1114908ar

Reclaiming Indigenous Sign Languages and Supporting Accessibility and Inclusion for Indigenous Deaf Children and their Families

2024· article· en· W4405108004 on OpenAlexaffvenue
Kristin Snoddon, D Ireland, Joel Abram, Elizabeth Osawamick, Miigwaans Osawamick-Sagessige

Bibliographic record

VenueFirst Peoples Child & Family Review An Interdisciplinary Journal Honouring the Voices Perspectives and Knowledges of First Peoples · 2024
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsIndigenousSign languageInclusion (mineral)Deaf communitySign (mathematics)Deaf cultureIndigenous languagePsychologyPolitical scienceSociologyGender studiesLinguistics

Abstract

fetched live from OpenAlex

This paper reports data from a research study and workshop about reclaiming Indigenous sign languages and cultures, and strengthening services for Indigenous deaf children and their families and communities. The purpose of this workshop was for presenters to share their lived experiences and knowledge as deaf and hearing Elders, parents, and youth, including what resources were and were not available to them. Findings revealed themes including the importance of support for accessibility and inclusion from First Nations political and community leadership; the importance of supporting children’s intersectional identities; the need for greater resources for First Nations communities to access services and supports for deaf children; and youth experiences of learning about deaf culture and sign language, and attending deaf schools. These findings also suggested innovative models for including 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)0.006
metaresearch head score (Gemma)0.009
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 score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.357
Teacher spread0.340 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

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