Bibliographic record
Abstract
Ewoldt suggested that deaf readers might be bypassing phonological decoding and that they might be developing reading comprehension skills by relying more on semantic processing than syntactic.The development of phonological and syntactic processing skills is related to instructional approach and the whole language versus phonics debate and to the ever-present debate over communication method.The current version of the communication debate is whether or not learning a signed language will provide a basis for learning to read and write a spoken language.Bilingual programs for students who are deaf in Australia, Canada, and the United States are based, at least tacitly, on the assumptions of Cummins's interdependence theory, which holds that general language and literacy skills in one language will transfer to a second language.The question is: Does Cummins's theory apply when one language is spoken and another signed?Carol Musselman provides a comprehensive review of recent investigations in three of the four categories mentioned above: the process of decoding print, specific knowledge of the target language (English), and broad experience in a base language (ASL).Here, there is space to comment only on the first category.She concludes that skilled reading by deaf students involves phonological decoding.However, it appears that deaf students' access to phonological representations is an outcome of learning to read rather than a prerequisite.If this is the case, deaf readers must use other means to hold print in short-term memory for word identification.The alternatives are orthographic (memory of the
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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 | 0.027 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 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".