O ensino de química para surdos: produção de planos de aula especializado para turmas inclusivas
Bibliographic record
Abstract
The present work aims to propose the creation of Specialized Classroom Methods (SCM) for deaf students, so that it is possible to strengthen the bonds between teachers and interpreters and to provide students with a better understanding of the chemical content. The creation of the SCM’s showed that for the teaching of inclusive classes of deaf students it is necessary to denote aspects inherent to the deaf culture, such as more imagistic classes, more phenomena and even adaptations of scientific terms that will allow a better understanding of what is being studied. It is concluded that the main key for a better development of inclusive classes is the communication between teacherinterpreter, since the communication channel between these parts improves the teacher understands the function of the interpreter, and the interpreter knows the objective of the teacher. Within the proposed creation of model plans, to analyze the learning mode of deaf students, from the point of view of interpreters and teachers, aiming to understand the failures in learning mechanisms and propose, within the SCM, new structures to reduce the learning gap of deaf students and listeners.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".