Identification and Remediation of Reading Difficulties Based on Successive Processing Deficits and Delay in General Reading
Bibliographic record
Abstract
Widespread learning problems among South African children are associated with the apartheid era and show a need for effective reading programs. In selecting these programs, it is useful to differentiate between children with dyslexia and children whose reading is poor because teaching was inadequate. In this study, the Woodcock Tests of Reading Mastery-Revised and tests modelled on the Cognitive Assessment System were used to define a group of children with deficits in successive processing associated with dyslexia and a group of children with general reading delay. There were two girls and five boys in each group. For the children with successive processing deficit, the mean age was 9 yr., 8 mo. For the other group, mean age was 9 yr., 3 mo. Control groups were matched for age and sex and kind of reading difficulty. The first group received Das's PASS Reading Enhancement Program, and the second participated in a remedial program based on Whole Language principles. The treatment groups received 24 1-hr. long sessions. Gains in successive processing were shown for the first group, as measured by the tests modelled on Cognitive Assessment System subtests but not for the second group. Both groups showed gains in phonics and word identification, relative to their respective control groups, suggesting the respective intervention program was effective for each group.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".