Prevalence of spelling errors affects reading behavior across languages.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This cross-linguistic study investigated the impact of spelling errors on reading behavior in five languages (Chinese, English, Finnish, Greek, and Hebrew). Learning theories predict that correct and incorrect spelling alternatives (e.g., "tomorrow" and "tommorrow") provide competing cues to the sound and meaning of a word: The closer the alternatives are to each other in their frequency of occurrence, the more uncertain the reader is regarding the spelling of that word. An information-theoretic measure of entropy was used as an index of uncertainty. Based on theories of learning, we predicted that higher entropy would lead to slower recognition of words even when they are spelled correctly. This prediction was confirmed in eye-tracking sentence-reading experiments in five languages widely variable in their writing systems' phonology and morphology. Moreover, in each language, we observed a characteristic Entropy × Frequency interaction; arguably, its functional shape varied as a function of the orthographic transparency of a given written language. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it