Bibliographic record
Abstract
We have analyzed many variables in Cantonese but not in other languages: classifier specialization, tone mergers, vowel splits and mergers, motion event expression, and (L > R), as well as (VOT) and (PRODROP). As little sociolinguistic work on any variety of this globally large language exists, these studies serve as useful models to expand variationist studies to languages that vary in many ways from the North American, Indo-European languages of focus to date. We show that classifiers are developing a specific semantic contrast (for number-marking) in Heritage Cantonese, amplifying a homeland trend; that three tone mergers that were reported to be completed are only partial, in both homeland and heritage varieties; that some vowel mergers and splits may be attributed to influence from English, but that changes in the constraints governing motion event expression cannot be attributed to simplification or English-contact effects. We report on covariation among the variables, showing that it is not the case that the same speakers lead change in each. Thus, it is not easy to claim that language proficiency or patterns of use are responsible for the variation. Rather, internal change and identity-marking motivations for change must be considered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.019 | 0.002 |
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".