The emergence of phonological adaptation from phonetic adaptation: English loanwords in Korean
Bibliographic record
Abstract
This paper provides a detailed diachronic account of the adaptation of the English posterior coronal obstruents /ʃ ʧ ʤ/ in Contemporary Korean. These consonants are variably adapted with a glide (/j/ or /w/), and the distribution of the glides is conditioned by phonetic and phonological characteristics of the English input, as well as native phonotactic restrictions. The diachronic change in the occurrence of /w/ serves as an example of a variable phonetic detail in the input that is faithfully represented in loans in earlier stages, but which is subsequently eliminated in the emerging norm. Given this data, I propose how what, on the surface, may appear to be a ‘phonological’ adaptation can arise through regularisation of what is essentially a ‘phonetic’ adaptation. This study highlights the complexity of loanword adaptation and the importance of examining all of the different factors shaping this process.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".