Rhotic Lenition as a Marker of a Dominant Character Type in Northern Mandarin Chinese
Bibliographic record
Abstract
This paper looks at social identity with respect to two types of rhotacization: vowel rhotacization, in which a vowel is r-coloured, and consonant rhotacization (i.e. rhotic lenition). Recent studies (Zhang 2005, Zhang 2008) have investigated character type as a sociolinguistic variable affecting rhotacization in Mandarin Chinese speech. Rhotacization, in turn, has sociocultural associations that differ by both geographic region and regional character types (Lee 2007). I argue that in Northern China, there is a correlation between rhotic lenition and dominant, particularly masculine, social identities. In this study, I interview thirteen Mandarin speakers from Henan Province, a distinctly northern—but not northeastern—prefecture. Participants are interviewed and possible lenited tokens are counted. I hypothesize that a positive correlation between identity and lenition will be seen in speakers who perceive themselves as having dominant personalities; that people who identify with a dominant character type will exhibit more tokens of consonant rhotacization in casual speech. To explain this phenomenon, I take the view that there is a prevalent linguistic ideology linking vowel rhotacization with rurality, low social class, and Northeastern identity. I will show that among speakers of Henan Mandarin, vowel rhotacization is an overt marker of this identity, whereas consonant rhotacization (i.e. rhotic lenition) is less overt. Rhotic lenition is a unique and critical variable which functions as a marker of a dominant character type without establishing a Northeast identity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".