The Development of SAH Reading Passage Compendium: A Tool for the Assessment of Reading Performance Related to Visual Function
Bibliographic record
Abstract
Passages with continuous sentences are commonly used for the assessment of reading performance related to visual function, and rehabilitation in optometric practices. Passages created in native languages are crucial for a reliable interpretation in a real scenario. This study aimed to report the development of SAH Reading Passage Compendium (SAHRPC), as a tool for the assessment of reading performance related to visual function. SAHRPC uses the Malay language as the medium of communication. The development of the SAHRPC encompassed three stages: exploratory, confirmatory and prototyping. In the exploratory stage, 300 sentences were extracted from the standard school textbook (in the Malay language) endorsed by the Ministry of Education, Malaysia. The accumulated reading materials were processed based on two deciding factors: continuous sentence structure and predetermined total number of words. A total of 56 passages were constructed with equal readability, based on a simple “5 continuous sentences structure of 50 words” combination. In the confirmatory stage, the 56 passages were verified by normal sighted native Malay speakers. The reading duration was measured using a stopwatch, while the errors were recorded using an audiotape. Reading speed was quantified in words per minute (wpm). Three passages were first eliminated based on the outliers present in the boxplot graph. Eleven passages were further eliminated based on the 10 % highest error and 5 % of the two extreme ends of the reading speed range. The remaining 42 passages with good reliability were randomly compiled into 3 sets of 13 passages. Thirteen passages in each set were randomly sorted into 13 print sizes, ranged from 1.2 logMAR to 0.0 logMAR. The interchangeability of the 3 sets was inspected and confirmed. A prototype was developed and packaged as the SAHRPC, to be used as a tool for the assessment of reading performance related to visual function, and rehabilitation purposes.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".