Comic Books: A Learning Tool for Meaningful Acquisition of Written Sign Language
Bibliographic record
Abstract
Deaf people use Sign Language (SL) for intellectual development, communications and other human activities that are mediated by language—such as the expression of complex and abstract thoughts and feelings; and for literature, culture and knowledge. The Brazilian Sign Language (Libras) is a complete linguistic system of visual-spatial manner, which requires an adequate writing system. The specificities of Libras pose a challenge for alphabetisation/literacy in the educational process of the Deaf, which allows for meaning attribution by the Deaf learner only when the SL is the central pedagogical tool. This process vastly differs from the pedagogical strategies used to teach the written form of the oral languages. The alphabetisation relies heavily on the phoneme-grapheme relations—therefore, not accessible to the Deaf learner. SignWriting is a writing system deemed to be adequate represent Libras, and has been used in literacy processes of Deaf learners. This article presents a visual pedagogical tool, a narrative in the genre of a comic book, to be used for meaningful learning and acquisition of SignWriting. The methodological approach of this research involved the creation of a comic book tool to provide the context and communicational situation where the enunciation in Libras occurs, thus presenting an opportunity to introduce SignWriting. The utterances are then presented in the form of writing activities. Results show that the use of the proposed Visual Narrative approach is an adequate educational strategy to inform the design of pedagogical practices for teaching the writing system of SL.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".