Bibliographic record
Abstract
Word-form speech errors are assumed to adapt to the unintended environment. For example, in the error ‘bads cat’ for intended ‘bad cats,’ the plural marker ‘s’ assimilates to the voicing of the ‘d’ in ‘bad.’ This phenomenon, called accommodation, provides evidence that the component responsible for errors is processed before the phonological assimilation component. Previous work on accommodation relies on the researcher to detect accommodation [D. Boomer and J. Laver, Br. J. Disord. Commun. 3, 1–12 (1968)]. Given that these studies are prone to perceptual bias, the conclusion that accommodation is the norm remains open. An acoustic analysis of errors was conducted to re-address this question. Thirty-two nonword tongue twisters, e.g., ‘tiff tivv tivv tiff,’ were designed to induce voicing errors on the coda. Because vowel lengthening before voiced codas is a phonological process in English, vowel length can be measured to detect accommodation. Errors were determined for each participant by measuring coda percent voicing and vowel duration in a control condition. Results from six participants (872 errors) show that while errors do accommodate (7.6%), nonaccommodation (40.7%) occurs more frequently. This result shows that errors do occur after phonological processing. [Work supported by OGS and Carleton University.]
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".