Bibliographic record
Abstract
The functional load hypothesis predicts that phonemes with lower functional load are more likely to merge than higher functional load phonemes. Using Cantonese spontaneous speech data from the Heritage Language Variation and Change Corpus (Nagy, 2011), this chapter addresses the functional load hypothesis in a heritage language context by comparing F1/F2 production patterns of two vowel pairs (i.e., /y/~/u/ and /a/~/ɔ/) across thirty-two speakers based on generational group (i.e., Gen0, Gen1, and Gen2) and dominant language (i.e., Cantonese versus English). An analysis of Pillai score values (Nycz & Hall-Lew, 2015) shows a significant decrease in the phonetic distinctiveness of /y/~/u/ for English-dominant speakers (i.e., all Gen2). The /y/~/u/ pair also has a lower functional load. Dominant language, however, was not a significant Pillai score predictor for /a/~/ɔ/. Overall, the results support the functional load hypothesis in a heritage language context and show how it can complement accounts of heritage languages based on dominant language transfer.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".