The Teaching and Learning of Third Tone Sandhi: L2- and Heritage-Learners of Mandarin Chinese in Canadian University Classes
Bibliographic record
Abstract
In this paper, we probe the question of whether heritage leaners (HLs) have a phonological (rather than just the documented phonetic) advantage in language classes. Polinsky (2015) argues adult HLs, while divergent in morphosyntax, have certain "phonological" advantages. Chang, Yao, Haynes and Rhodes (2011) argue that HLs are more nativelike than second language (L2) learners in producing certain phonetic details. We explore the teaching and learning of Chinese T3 tone sandhi (i.e., a phonological feature that learners must acquire). Given that T3 has been argued to be the most problematic tone in both L2 perception and production among all lexical tones (Zhang, 2014(Zhang, , 2016)), we probe how "good" their sandhi pronunciation is. Our data show that the Mandarin HLs do not have a phonological advantage over (i.e., are not significantly different from) non-heritage L2 learners. Furthermore, we show that Cantonese HLs are significantly less comprehensible than non-heritage L2 learners. Little time is devoted to pronunciation in language teaching This is true in many Chinese classes in Canadian universities. Common textbooks (e.g., Integrated Chinese) emphasize vocabulary and grammar. Chinese instructors in Canadian universities face the challenge of having a mixed student population: heritage language learners (HLs) and non-heritage L2 learners. This can lead to high levels of anxiety in the HLs in the classroom (Prada & Guerrero-Rodriguez, 2020). Teachers need to be aware of this HL anxiety and cannot assume that HLs will be "experts" in their class.
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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 | 0.003 | 0.001 |
| 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 | 0.002 |
| 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".