AS EXPERIÊNCIAS DE INCLUSÃO/EXCLUSÃO DAS PRIMEIRAS MESTRAS SURDAS NA PÓS-GRADUAÇÃO EM LETRAS NA UNIVERSIDADE FEDERAL DO ESPÍRITO SANTO
Bibliographic record
Abstract
This article analyzes the inclusion of deaf students in the Graduate Program in Language Studies (PPGL) at UFES. It highlights the importance of Brazilian Sign Language (Libras) and the experiences of the first deaf female graduate students at the institution, who defended their theses in 2023. The theoretical framework is based on critical studies of education under neoliberal logic (BORGES, 2022; OLIVEIRA; CARVALHO, 2021; LAVAL, 2004; HOLANDA, 1998) to discuss the challenges of inclusion in postgraduate education, such as the language barrier and the need for interpreters, emphasizing the role of institutional support and innovative pedagogical practices. The authors explore the history of inclusive education in higher education and the need for continuous investment in effective policies, proposing a reformulation of funding and educational actions to ensure accessibility in Libras.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".