How Do Profoundly Deaf Children Learn to Read?
Bibliographic record
Abstract
Reading requires two related, but separable, capabilities: (1) familiarity with a language, and (2) understanding the mapping between that language and the printed word (Chamberlain & Mayberry, 2000; Hoover & Gough, 1990). Children who are profoundly deaf are disadvantaged on both counts. Not surprisingly, then, reading is difficult for profoundly deaf children. But some deaf children do manage to read fluently. How? Are they simply the smartest of the crop, or do they have some strategy, or circumstance, that facilitates linking the written code with language? A priori one might guess that knowing American Sign Language (ASL) would interfere with learning to read English simply because ASL does not map in any systematic way onto English. However, recent research has suggested that individuals with good signing skills are not worse, and may even be better, readers than individuals with poor signing skills (Chamberlain & Mayberry, 2000). Thus, knowing a language (even if it is not the language captured in print) appears to facilitate learning to read. Nonetheless, skill in signing does not guarantee skill in reading—reading must be taught. The next frontier for reading research in deaf education is to understand how deaf readers map their knowledge of sign language onto print, and how instruction can best be used to turn signers into readers.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".