Detecting spelling errors in compound and pseudocompound words.
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).
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".