Aligning the timelines of phonological acquisition and change
Bibliographic record
Abstract
This paper examines whether data from a large cross-linguistic corpus of adult and child productions can be used to support an assumed corollary of the Neogrammarian distinction between two types of phonological change. The first type is regular sound change, which is assumed to be incremental and so should show continuity between phonological development and the age-related variation observed in the speech community undergoing the change. The second type is dialect borrowing, which could show an abrupt discontinuity between developmental patterns before and after the socio-historical circumstances that instigate it. We examine the acquisition of two contrasts: the Seoul Korean contrast between lax and aspirated stops which is undergoing regular sound change, and the standard Mandarin contrast between retroflex and dental sibilants which has been borrowed recently into the Sōngyuán dialect. Acquisition of the different contrasts patterns as predicted from the assumed differences between continuous regular sound change and potentially abrupt dialect borrowing. However, there are substantial gaps in our understanding both of the extent of cross-cultural variability in language socialization and of how this might affect the mechanisms of phonological change that must be addressed before we can fully understand the relationship between the time courses of the two.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".