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Record W4406704839 · doi:10.22533/at.ed.5585225160114

PUBLIC POLICIES AND EXPERIENCES IN SPECIALIZED EDUCATIONAL CARE BASED ON THE BILINGUAL APPROACH FOR STUDENTS WITH DEAFBLINDNESS AND THEIR FAMILIES IN THE MUNICIPALITY OF RIO DAS OSTRAS/RJ

2025· article· en· W4406704839 on OpenAlexfundno aff
Flávia Regina França Pascoal de Oliveira

Bibliographic record

VenueInternational Journal of Human Sciences Research · 2025
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsnot available
FundersPrincess Margaret Cancer Foundation
KeywordsBilingual educationPublic educationSociologyPsychologyPedagogyPolitical sciencePublic administration

Abstract

fetched live from OpenAlex

This work contributes to pedagogical practice and the changes that have occurred throughout the historical process in relation to Public Policies aimed at people with disabilities.The general objective is to reflect on Specialized Educational Assistance as a Public Policy in the area of Special Education, based on the keywords: Public Policies; Specialized Educational Assistance; Bilingual Education; Visual Pedagogy; Brazilian Sign Language.Bibliographic research and action research were used in the studies.Throughout the work, it is proposed to understand the Historical Process and Public Policies of Special Education based on reality; to share the experiences of deaf people and their families; and to identify the contributions of new Technologies (ICTs).The final considerations include the significant pedagogical work done for deaf students in Specialized Educational Assistance (AEE) and their families, as well as the possibility of further research into public policies.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.008
Scholarly communication0.0040.002
Open science0.0010.006
Research integrity0.0020.003
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 head0.340
GPT teacher head0.573
Teacher spread0.233 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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