Detecting spelling errors in compound and pseudocompound words.
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
). In half of the compound and pseudocompound words, spelling errors were created by transposing adjacent letters and in half of the control words, errors were created by transposing letters at the same location as the matched compound or pseudocompound words. Correctly spelled compound words were more easily processed than matched control words, but this advantage was removed when letter transpositions were introduced at the morpheme boundary. In contrast, misspelled pseudocompound words showed a processing deficit relative to their matched control words when letter transpositions were introduced at the (pseudo)morpheme boundary. The results strongly suggest that morphological processing is attempted obligatorily when the orthography indicates that morphological structure is present. However, the outcomes of the morphological processing attempts are different for compounds and pseudocompounds, as might be expected, given that only the compounds have a morphological structure that matches the structure suggested by the orthography. The findings reflect 2 effects: an orthographic effect that is facilitatory and not sensitive to morphological structure of the whole word, and a morphemic effect that is facilitatory for compounds but inhibitory for pseudocompounds. (PsycINFO Database Record (c) 2020 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 | 0.001 |
| 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 it