Acquiring Rhythm: A Comparison of L1 and L2 Speakers of Canadian English and Japanese
Bibliographic record
Abstract
Lacking knowledge of either language, one can readily distinguish Spanish and German speech, but distinguishing Italian and Spanish is more difficult. One reason that certain pairs of languages sound distinct is that they have different rhythms. The rhythmic contrast between, for example, Spanish and German arises in part from durational differences between stressed and unstressed vowels, and from the complexity of permissible syllable structures (Dauer, 1983). In German or English, for instance, the alternation between long, stressed vowels and short, reduced vowels is said to create a percept of a contrastive or “Morse code ” rhythm (Lloyd James, 1940). Conversely, the much lower degree of stress-related lengthening, the relative attenuation of vowel reduction in unstressed syllables, and the lower frequency of complex consonant clusters in Spanish contribute to the impression of a more regular or “machine gun ” rhythmic pattern, at least to speakers of English or German. Based on these observations, a number of acoustic metrics for speech rhythm have been proposed (e.g. Dellwo & Wagner, 2003; Grabe & Low, 2002;
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".