A beginning exploration of text generation abilities in university students with a history of reading difficulties
Bibliographic record
Abstract
There is a fundamental lack of understanding of how university students with a history of reading difficulties perform on various demanding literacy tasks. We compared the text generation skills, measured with timed summary writing and proofreading tasks, of university students with a history of reading difficulties to those of students with no such history. We further examined whether between-group differences in text generation skills remained after controlling for transcription skills (spelling and handwriting fluency), word reading, and reading comprehension. Forty-six university students with a history of reading difficulties were matched on age, gender, and non-verbal intelligence to 46 students without this history. We found that the students with a history of reading difficulties performed poorer on both measures of text generation than students without this history. When differences in transcription skills, word reading, and reading comprehension were controlled, we found that only differences in timed summary writing remained significant. These results suggest that students with a history of reading difficulties experience challenges with specific aspects of text generation that are beyond what one would expect from their difficulties with transcription and word reading. We suggest that, if not addressed, text generation deficits are likely to create obstacles for academic success.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".