Perception of natural vowels by monolingual Canadian-English, Mexican-Spanish, and Penninsular-Spanish listeners
Bibliographic record
Abstract
On the basis of a previously-reported synthetic-vowel perception experiment, it was hypothesized that the location of the perceptual boundary between Spanish /i/ and /e/ differed for monolingual Peninsular-Spanish and Mexican-Spanish listeners (north-central Spain and Mexico City), and that this would affect the perception of the Canadian-English /i/-/i/ contrast (western Canada): Peninsular-Spanish listeners were predicted to identify almost all tokens of Canadian-English /i/ as Spanish /i/ and almost all tokens of Canadian-English /i/ as Spanish /e/ (two-category assimilation); whereas Mexican-Spanish listeners were predicted to identify almost all tokens of Canadian-English /i/ as Spanish /i/, but identify some tokens of Canadian-English /i/ as Spanish /i/ and some as Spanish /e/. Monolingual Peninsular-Spanish and Mexi-can-Spanish listeners’ perception of natural tokens of English /i/, /i/, /e/, and /e/ produced by monolingual Canadian-English speakers was tested. Both the Peninsular-Spanish and the Mexican-Spanish listeners had results consistent with the perceptual pattern predicted for the Peninsular-Spanish listeners. The results call into question the assumption that first-language-Spanish learners of English have difficulty learning the English /i/-/i/ contrast because they initially assimilate most tokens of both English vowel categories to a single Spanish vowel category, Spanish /i/.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".