Bibliographic record
Abstract
Languages lenite similar segments to different extents—a finding that can be accounted for with information-theoretic variables like frequency, predictability, and informativity [Cohen Priva, 2017, Language 93: 569–597]. Prior research addresses the role of segment information content across languages, but assumes that information-theoretic variables operate on an in-language basis. While appropriate for monolingual speech, this assumption is problematic for bilingual speech, as an individual’s languages are known to influence one another (e.g. [Fricke et al., 2016, J. Mem. Lang. 89: 110–137]). In this paper, I report on a study using the Bangor Miami corpus [Deuchar et al., 2014, Advances in the Study of Bilingualism: 93–110], addressing how well information-theoretic variables predict lenition in Spanish-English bilingual speech. Specifically, do they operate on an in-language or cross-language basis? To address this, I focus on the duration of word-medial intervocalic fricatives shared by both languages—/f/ and /s/. Accounting for variables known to affect segment duration (e.g. speech rate), I use linear mixed-effect models to assess the contribution of information-theoretic variables in accounting for duration variation in bilingual speech. Four models are evaluated, compared, and discussed: (i) English in-language, (ii) English cross-language, (iii) Spanish in-language, and (iv) Spanish cross-language.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".