The adaptation of the English voiceless affricate in 1930s Korean
Bibliographic record
Abstract
Normal.dotm 0 0 1 183 1046 University of Toronto 8 2 1284 12.0 0 false 18 pt 18 pt 0 0 false false false In this paper, we examine the adaptation of English / t ? / in Korean loanwords in the 1930s. The overall adaptation pattern found in the 1930s data is similar to the pattern found in Contemporary Korean; the affricate was adapted as /c h / and an epenthetic vowel (/i/) was inserted when the affricate occurred in non-prevocalic position in the English input. Also, similar to Contemporary Korean, a palatal glide was often inserted in the transcription even though the available evidence suggests that the glide d id not surface in the pronunciation. We argue that the distribution of in the transcription is not arbitrary ; rather, it follows the co? occurrence restrictions of in the native words and the transcription is a reflection of a level of phonological representation that is somewhat more abstract than the surface pronunciation. The only crucial difference found in the data between 1930s and contemporary Korean was that was frequently inserted in the 1930s data, due to the rounding gesture of the affricate in English , but the usage of in transcription or pronunciation is extremely rare in contemporary Korean. This indicates that loanword adaptation was more sensitive to phonetic details in the 1930s than in contemporary Korean.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".