Bibliographic record
Abstract
Unlike many communities studied by variationists in which SLI means convergence of spoken and written language, for Cantonese speakers, language ideology historically meant the opposite. In this presentation, I show how traces of this ideological distinction between written and spoken codes (cf. Snow 2004) remain present in both Hong Kong and in Toronto. I focus on two sets of sound correspondences found with (Standard) Mandarin cognates: Cantonese /i/ to Mandarin /ə/ and Cantonese /y/ to Mandarin /u/). Only Cantonese /y/ shows convergence, but only for second-generation Toronto speakers who are least likely to speak Mandarin. Toronto English influence, thus, better accounts for this change. The lack of standard Chinese influence on vowel pronunciation can be understood in terms of a historic distinction between codes and in terms of how speakers understand these distinctions (as I show from sociolinguistic interview excerpts) even as these distinctions continue to evolve under Western influence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".