The Effects of Language Contact on Non-Native Vowel Sequences in Lexical Borrowings: The Case of Media Lengua
Bibliographic record
Abstract
Media Lengua (ML), a mixed language derived from Quichua and Spanish, exhibits a phonological system that largely conforms to that of Quichua acoustically. Yet, it incorporates a large number of vowel sequences from Spanish which do not occur in the Quichua system. This includes the use of mid-vowels, which are phonetically realized in ML as largely overlapping with the high-vowels in acoustic space. We analyze and compare production of vowel sequences by speakers of ML, Quichua, and Spanish through the use of generalized additive mixed models to determine statistically significant differences between vowel formant trajectories. Our results indicate that Spanish-derived ML vowel sequences frequently differ significantly from their Spanish counterparts, largely occupying a more central region of the vowel space and frequently exhibiting markedly reduced trajectories over time. In contrast, we find only one case where an ML vowel sequence differs significantly from its Quichua counterpart-and even in this case the difference from Spanish is substantially greater. Our findings show how the vowel system of ML successfully integrates novel vowel sequence patterns from Spanish into what is essentially Quichua phonology by markedly adapting their production, while still maintaining contrasts which are not expressed in Quichua.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".