The primacy of morphology in English braille spelling: an analysis of bridging contractions
Bibliographic record
Abstract
Abstract This study examines the use of braille contractions in a corpus of spelling tests from braille-reading children in grades 1-4, with particular attention to braille contractions that create mismatches with morphological structure. Braille is a tactile writing system that enables people who are blind or visually impaired to read and write. In English and many other languages, reading and writing braille is not simply a matter of transliterating between print letters and their braille equivalents; Unified English Braille (the official braille system used in the United States, Canada, the United Kingdom, and several other English-speaking countries) contains 180 contractions—one or more braille cells that represent whole words or strings of letters. In some words, the prescriptive rules for correct braille usage cause contractions to bridge morphological boundaries and to obscure the spellings of stems and affixes. We demonstrate that, when the prescriptive rules for correct braille usage flout morphological structure, young braille spellers generally follow the morphology rather than the orthographic rules. This work establishes that morphology matters for young braille learners. We discuss the potential impact of our findings on braille research, development, and pedagogy, and we suggest ways in which our findings contribute to understanding the nature of orthographic morphemes and the place of braille in the reading sciences.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".