Validation of the International Reading Speed Texts in a Canadian Sample
Bibliographic record
Abstract
Purpose: The purpose of these studies are (1) to validate the IReST in an English-speaking Canadian sample; and (2) examine how reading comprehension questions and reduced visual acuity effect reading speed on the IReST.Materials & Methods: Study 1: Canadian English speakers (n=25) read all 10 IReST following the procedures used in the original IReST validation. 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, Mdiff=[-5.30,11.43], Cohen’s d=[-.15, .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% CI=[-16.7,-34.1]) and in the simulated impairment condition (Mdiff=59.3, 95% CI [-47.7,-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 overestimate an individual’s true reading speed in individuals with normal/corrected-to-normal vision and reduced visual acuity.
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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 | 0.008 | 0.025 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".