Bibliographic record
Abstract
Dyslexia is a reading disability affecting a large number of people worldwide. People with dyslexia have at least normal levels of intelligence, yet they nevertheless have significant difficulties with reading. Dyslexia is known to have genetic causes; however, some researchers believe that there are also environmental factors at play. Specifically, the way in which a child is taught to read can possibly influence whether or not that child ultimately ends up with dyslexia or other reading difficulties. This paper presents the hypothesis that the way a child is taught to read can be a major factor in the development of dyslexia. There has been speculation about this idea in previous literature, but that speculation has been based only on anecdotes and case studies; empirical research is added in this paper. This hypothesis is based on research showing that certain approaches to teaching reading can induce difficulties with phonetic skills (in other words, difficulty associating written letters with spoken sounds in a language that uses an alphabetic writing system) and fMRI-measurable brain patterns matching those of people with dyslexia. Additionally relevant is that those approaches are widespread in the English-speaking world—most specifically in the United States. Reading difficulties not related to phonetic skills would not be implicated in this hypothesis. The literature justifying this hypothesis is discussed, as well as the challenges to the hypothesis and a way that it can be tested. The importance of proposing this hypothesis is that if flawed reading instruction is indeed one primary cause of dyslexia, then reform in elementary schools is vital.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".