How Do Deaf Signers of LSQ and Their Teachers Construct the Meaning of a Written Text?
Bibliographic record
Abstract
Many studies have investigated why learning to read is so problematic for deaf individuals. However, we still know very little about how to teach reading to signing students. In this article, we report on an exploratory qualitative study of deaf LSQ (Langue des signes québécoise) signers learning to read with two teachers, in an effort to better understand what strategies might be most useful in constructing the meaning of a text. By videotaping reading sessions between each teacher and student, then conducting recall interviews, we found that both students and teachers used a number of strategies to construct meaning. The list of strategies observed was categorized as word attack or global meaning types. Developing readers showed different patterns of strategy use, with more global meaning strategies being used by the more independent reader. We also found that the deaf teacher and hearing teacher had different patterns of strategy use, although both favored global meaning types. Finally, our findings indicate that both teachers adapted their strategy use to the needs of the students, but with a different focus. Namely, the deaf teacher used more global meaning strategies with the weaker reader and less with the more independent reader, whereas the hearing teacher showed the opposite pattern.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".