The role of predictability in shaping phonological patterns
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
Abstract A diverse set of empirical findings indicate that word predictability in context influences the fine-grained details of both speech production and comprehension. In particular, lower predictability relative to similar competitors tends to be associated with phonetic enhancement, while higher predictability is associated with phonetic reduction. We review evidence that these in-the-moment biases can shift the prototypical pronunciations of individual lexical items, and that over time, these shifts can promote larger-scale phonological changes such as phoneme mergers. We argue that predictability-associated enhancement and reduction effects are based on predictability at the level of meaning-bearing units (such as words) rather than at sublexical levels (such as segments) and present preliminary typological evidence in support of this view. Based on these arguments, we introduce a Bayesian framework that helps generate testable predictions about the type of enhancement and reduction patterns that are more probable in a given language.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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