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Record W4387885156 · doi:10.29327/2323543.22.1-24

A FUNÇÃO PEDAGÓGICA DO LEDOR / TRANSCRITOR PARA O ALUNO COM DEFICIÊNCIA VISUAL

2023· article· pt· W4387885156 on OpenAlexaff
Kelly Cristina Soares Maia, Ronaldo de Araujo Maia

Bibliographic record

VenueRevista Científica Excellence · 2023
Typearticle
Languagept
FieldEnvironmental Science
TopicEnvironmental Sustainability and Education
Canadian institutionsEagle Ridge Hospital
Fundersnot available
KeywordsArtPsychologyPhilosophy

Abstract

fetched live from OpenAlex

INTRODUÇÃO: Neste artigo estaremos abordando a função pedagógica do ledor / transcritor para o aluno com deficiência visual, inicialmente faremos uma retrospectiva histórica da deficiência visual, as primeiras iniciativas de inclusão social, econômica e educacional de pessoas com deficiência visual, em seguida veremos números que demonstram que as quantidades de pessoas atingidas demandam um esforço social neste processo de inclusão e finalmente abordaremos a função pedagógica do ledor / descritor na formação deste aluno com necessidades visuais especiais.METODOLOGIA: Trata-se de uma pesquisa de natureza qualitativa, com abordagem descritiva, trazendo cunho bibliográfico diante da deficiência visual e abordagens históricas diante da temática.CONSIDERAÇÕES FINAIS: Considerando a relevância e poucos materiais acadêmicos publicados, a temática traz a luz de questões importantes do papel do pedagógico para que se possa melhorar e aperfeiçoar profissionais para o desenvolvimento do processo ensino aprendizagem do alunado, e esse aperfeiçoamento profissionais tragam melhorias na didática para o desenvolvimento intelectual do aluno com as questões de deficiência visual.

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)0.007
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.005
Scholarly communication0.0050.004
Open science0.0020.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0160.005

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 head0.027
GPT teacher head0.295
Teacher spread0.269 · 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 designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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