Native and Non-Native Patterns in Conflict: Lexicon vs. Grammar in Loanword Adaptation in Brazilian Portuguese
Bibliographic record
Abstract
English loanwords with /ʌ/ that are part of the Brazilian Portuguese (BP) lexicon are normally produced with [ɐ] (pub: [ˈpɐbɪ]). Although [ɐ] is the closest segment to /ʌ/ in the native inventory, it is highly constrained in BP: it is an allophone of /a/ that can only appear in nasal contexts. This paper investigates whether native speakers of BP generalize to novel loanwords the adaptation pattern of English /ʌ/ that is present in the BP lexicon. Two experiments were conducted, one with real loanwords and one with nonce loanwords. In the Real Loanword Experiment, participants consistently used [ɐ] both in oral (pub) and nasal contexts (funk), as predicted given the patterns in the lexicon. In the Nonce Loanword Experiment, participants used [ɐ] significantly more frequently in nasal contexts – in oral contexts, the most frequent adaptation was [a]. This reveals that speakers employ their native grammar to filter new loanwords: in contexts where [ɐ] is not licensed, they favor the corresponding licensed form. These results suggest that native speakers do not generalize non-native patterns that are present in the lexicon, mirroring what has been observed for the generalization of unnatural patterns in native grammars (e.g., Garcia 2017; Jarosz 2017).
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".