Marginal representations in loanword adaptation: affrication in Brazilian Portuguese English
Bibliographic record
Abstract
In loanword adaptation, it has been argued that category proximity is preferred over phonetic approximation (LaCharité and Paradis, 2005). For example, building and cook are adapted to Spanish as b[i]lding and c[u]k, respectively, even though English lax {i u} are phonetically closer to /e o/ than to /i u/ in Spanish (Delattre, 1981). If phonetic approximation were the main factor in these adaptations, we would expect *b[e]lding and *c[o]k, which in turn would modify the value of the feature [high] in the vowels, selecting a different existing phonological category in the target language. Similar adaptations have been shown in other languages. In this paper, we consider a context of loanword adaptation in Brazilian Portuguese (BP) where category proximity does not apply due to the allophonic status of the adapted forms. Such a scenario yields a pattern of adaptation that is mostly driven by phonetic proximity, even though it results in forms that are not part of the borrowing system. This suggests that phonological representations in loanword adaptation may involve an expansion of the borrowing system.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".