Bibliographic record
Abstract
This paper examines how native English speakers acquire stress in Portuguese. Native speakers and second language learners (L2ers) of any given language have to formulate word-level prosodic generalizations based on a subset of lexical items to which they have been exposed. This subset contains robust as well as subtle cues as to which stress patterns are more or less productive, so that when speakers encounter novel forms they know which stress position is more likely. L2ers, however, face a much more challenging task, mainly if they are adults and have long passed the critical period. These difficulties are particularly notable in word-level prominence, where several interacting phonetic cues are involved. The trends observed across three proficiency levels in the judgement task described in this paper are consistent with a foot-based analysis, and show that L2ers successfully reset extrametricality (Yes in the L1; No in the L2) and shift the default stress position from antepenult (L1) to penult (L2). The latter is expected to follow from the former in a foot-based approach where feet become aligned to the right edge of the word as extrametricality is reset to No.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".