Validation of the International Reading Speed Texts in a Canadian Sample
Bibliographic record
Abstract
SIGNIFICANCE: The International Reading Speed Texts (IReST) is a valid measure of reading speed in a Canadian sample. However, if clinicians desire to assess reading comprehension using the IReST, this will significantly reduce reading speeds of individuals with normal vision or reduced visual acuity and therefore should use the values presented here. PURPOSE: The purposes of this study are (1) to validate the IReST in an English-speaking Canadian sample and (2) to examine how reading comprehension questions and reduced visual acuity affect reading speed on the IReST. METHODS: For study 1, Canadian English speakers (n = 25) read all 10 IReST following the procedures used in the original IReST validation. For study 2, Canadian English speakers (n = 50) read all 10 IReST, half with normal/corrected-to-normal vision and half with reduced visual acuity, and were asked reading comprehension questions. RESULTS: No significant differences were found between Canadian sample and the published IReST values (in all cases, P > .05; mean difference [Mdiff] = -5.30 to +11.43; Cohen d = -0.15 to +0.27; Bayes factors = 0.41, 0.09). Assessing reading comprehension with multiple-choice questions on the IReST significantly reduced reading speeds in the normal vision condition (Mdiff = 25.3; 95% confidence interval, -16.7 to -34.1) and in the simulated impairment condition (Mdiff = 59.3; 95% confidence interval, -47.7 to -71). CONCLUSIONS: The IReST is a valid measure that can be used to assess reading speed in a Canadian English-speaking sample. If researchers/clinicians wish to assess both reading speed and comprehension, using multiple-choice reading comprehension questions, then the values provided by the IReST will likely underestimate an individual's true reading speed in individuals with normal/corrected-to-normal vision or reduced visual acuity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".