Insights from a dyslexia simulation font: Can we simulate reading struggles of individuals with dyslexia?
Bibliographic record
Abstract
Individuals with dyslexia struggle at explaining what it is like to have dyslexia and how they perceive letters and words differently. This led the designer Daniel Britton to create a font that aims to simulate the perceptual experience of how effortful reading can be for individuals with dyslexia ( http://danielbritton.info/dyslexia ). This font removes forty percent of each character stroke with the aim of increasing reading effort, and in turn empathy and understanding for individuals with dyslexia. However, its efficacy has not yet been empirically tested. In the present study, we compared participants without dyslexia reading texts in the dyslexia simulation font to a group of individuals with dyslexia reading the same texts in Times New Roman font. Results suggest that the simulation font amplifies the struggle of reading, surpassing that experienced by adults with dyslexia—as reflected in increased reading time and overall number of eye movements in the majority of typical readers reading in the simulation font. Future research could compare the performance of the Daniel Britton simulation font against a sample of beginning readers with dyslexia as well as seek to design and empirically test an adapted simulation font with an increased preserved percentage of letter strokes [Correction added on 10 December 2021, after initial online publication. Abstract has been added].
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".