Measures of phonological typicality
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Bibliographic record
Abstract
Phonological Typicality (PT) is a measure of the extent to which a word’s phonology is typical of other words in the lexical category to which it belongs. There is a general coherence among words from the same category in terms of speech sounds, and we have found that words that are phonologically typical of their category tend to be processed more quickly and accurately than words that are less typical. In this paper we describe in greater detail the operationalisation of measures of a word’s PT, and report validations of different parameterisations of the measure. For each variant of PT, we report the extent to which it reflects the coherence of the lexical categories of words in terms of their sound, as well as the extent to which the measure predicts naming and lexical decision response times from a database of monosyllabic word processing. We show that PT is robust to parameter variation, but that measures based on PT of uninflected words (lemmas) best predict response time data for naming and lexical decision of single words.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it