Reading Speed Using the International Reading Speed Texts in a Normal Canadian Cohort
Bibliographic record
Abstract
Canadian cohort in Ontario was prospectively recruited with age >14 years, education >9 years, English as the primary language, and best-corrected visual acuity >20/25 distance and >N8 near in each eye. Participants with eye conditions and neurological/cognitive problems were excluded. Each participant consecutively read two IReST passages (passages 1 and 8). Reading speed in words per minute (WPM) was calculated. One-sample t-test was used to compare our cohort to published IReST standards. Results A total of 112 participants were included (35 male, 77 female). The mean age was 40 ± 17 years (14-18 years: 12; 18-35 years: 34; 35-60 years: 53; 60-75 years: 13). The mean reading speed for passage 1 was 211 ± 33 WPM compared to the published IReST standard of 236 ± 29 WPM (p < 0.0001). The mean reading speed for passage 8 was 218 ± 34 WPM compared to the IReST standard of 237 ± 24 WPM (p < 0.0001). Thus, our cohort read slower for both passages compared to IReST standards. The mean reading speed for passages 1 and 8 was the highest for the 14-18-year (231 and 239, respectively) and the lowest for the 60-75-year group (195 and 192, respectively). Conclusions Normal older populations have slower reading compared to younger populations. The slower reading in our cohort may also be because the passages were in British rather than in Canadian English. It is important that the IReST is evaluated in different populations to ensure reliable comparison standards for future research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".