Bibliographic record
Abstract
Research questions: Polinsky argues that speech rate in heritage languages is highly correlated with proficiency level. In sociolinguistics studies, speech rate in monolingual speakers is found to be conditioned by social factors. What occurs when both proficiency and social factors vary? Is speech rate a valid measure of proficiency? Methodology: We use two automated methods of measuring articulation rate (syllables per second), cross-referenced to improve accuracy: an orthographic vowel count and an acoustic measure of amplitude changes from syllable nucleus to periphery. Data and analysis: Across 51 speakers, each recorded in an hour-long conversation in Heritage Italian, Russian, Ukrainian, or Homeland Italian, we calculate speech rate in more than 10,000 clauses. Findings: Linear regression analyses reveal that articulation rate correlates with generation (since immigration) and age, but, surprisingly, not with ethnic orientation, sex or language. Age and generation are partly collinear in our sample, and models with generation fit better than those with age. We also find that articulation rate does not predict performance on sociolinguistic variables (voice onset time for stops, subject pronoun presence) in heritage varieties. Originality: This study compares two ways of calculating articulation rate automatically, examining whether speech rate is a viable stand-in for proficiency when social factors and proficiency vary independently. We resolve several obstacles to using articulation rate as a stand-in for more labor-intensive proficiency measures in spontaneous speech data. Implications: These findings suggest that speech rate is a valid proxy for heritage language proficiency. The factor with the strongest effect is generation since immigration (indicating the dominant language in the speaker’s childhood community). The effects of the social factors are complex but must be considered in order to interpret the proficiency measure accurately.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".