<i>Création du logiciel d’alphabétisation bilingue pour les Sourds “Le français sur le bout des doigts”: évaluation de l’outil et de la démarche de développement</i>
Bibliographic record
Abstract
Designed for a literate, hearing clientele, traditional CALL tools do not necessarily meet the needs of deaf people, and are not adapted to their learning styles, especially in the area of literacy. Current developments lead us to believe that, in order for deaf people to subscribe to literacy campaigns and to ensure that such measures are efficient, two conditions must be met: Quebec Sign Language must be the language of instruction (Dubuisson et al., 1997) and the participation of deaf people must be felt at every stage of the development of course material. Research has shown that in architecture, for example, the participation of the target clientele in the design process of the product can lead to the emergence of significant solutions (Vezeau et al., 1999). In light of the quantity of Web systems and products that are hardly used or difficult to use, Rubin (1994) reminds us of the need to consider the user, and not only the machine or the system, in the development process. The main goal of our research is to establish design parameters (developmental process, type of software, and content) for CALL software aimed at deaf adults. Only the data relating to the developmental process will be presented here. We will analyze and discuss the responses obtained through interviews with deaf members of the development team, audiotapes (on which an interpreter recorded the words of the team members), and videotapes of meetings. The interpretation of this data will give way to a qualitative assessment of the efficiency of the approach in the development of material adapted to the needs of the target population.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".