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Record W2143750685 · doi:10.1353/aad.2006.0029

Demographics of Deaf Education: More Students in More Places

2006· article· en· W2143750685 on OpenAlexaboutno aff
Ross E. Mitchell, Michael A. Karchmer

Bibliographic record

VenueAmerican annals of the deaf · 2006
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsDemographicsScrutinyAccountabilityEthnic groupCurriculumDeaf educationIntervention (counseling)Quarter (Canadian coin)PopulationSpecial educationMedical educationHearing lossPsychologyPublic relationsPolitical scienceMedicinePedagogySociologyGeographySign languageDemographyEnvironmental healthNursingAudiology

Abstract

fetched live from OpenAlex

We have witnessed important changes in the demographics of the deaf and hard of hearing student population receiving special education services during the past quarter century. The ethnic, intervention, and educational setting profiles are more diverse and dispersed. On top of the federal policy changes driving emerging intervention and continuing educational setting changes, there is now an increasing demand for deaf and hard of hearing students to participate in the general curriculum and school accountability systems. Over the same time period, stricter control over student data privacy and greater scrutiny of human subject protection have been incorporated into federal regulations. We highlight some consequences for deaf education research resulting from the convergences of these parallel trends in changing demographics, shifting school policy contexts, and regulation of federally funded research.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0200.002

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 head0.034
GPT teacher head0.408
Teacher spread0.374 · 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

Citations120
Published2006
Admission routes1
Has abstractyes

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