French loanwords in Vietnamese: the role of input language phonotactics and contrast in loanword adaptation
Bibliographic record
Abstract
This study examines the adaptation of French vowels in Vietnamese focusing on adaptation patterns that seem to defy a straightforward analysis based on native phonotactic restrictions or comparison of phonetic input-output similarity. A proper analysis requires reference to knowledge of the input language phonology. In the first case study, we observe that Vietnamese adapters extend the French phonotactic tendencies, i.e., Loi de Position, to loan adaptation productively. Such “intrusion” of L2 phonology knowledge may arise when phonetics underdetermines the adaptation and the adapters look to their knowledge of L2 phonology to arrive at adaptation. It is also notable that the L2 knowledge employed in adaptation is not native-like as the adaptation is not always isomorphic to the French input. In the second case study, the contrast of L2 phonology (/ʁ/ vs. /k/) is neutralized due to an L1 phonological restriction (i.e., no /ʁ/ in Vietnamese coda) but the Vietnamese adaptation systematically retains the contrast in the quality and length difference in the preceding vowel. There is plausible phonetic motivation for this adaptation pattern, but phonetically faithful mapping underdetermines the attested adaptation pattern, and reference to knowledge of L2 phonological contrasts is necessary. These findings illustrate the complexity of the loanword adaptation process, where a variety of different factors including L1 phonological restrictions, phonetic similarity, and L2 phonological knowledge, interact to affect adaptation.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".