Bibliographic record
Abstract
This article describes the way compliments and compliment responses are executed in Kunming Chinese, a Mandarin dialect spoken in Kunming, China. By looking at data collected through DCT questionnaires and natural observations, the author examines the semantic formulas used in forming compliments and compliment responses and the syntactic patterns of compliments in the two types of data. It is found that explicit compliments are the most common form of complimenting in the dialect. Implicit compliments, on the other hand, are much rarer and tend to occur by themselves. Syntactically, over 90% of the compliments fall into one of 4 syntactic structures paid through the third person/impersonal or second person perspectives. In replying to a compliment, speakers of Kunming Chinese are found to be drifting away from the tradition of rejecting compliments outright. They are more willing to accept compliments now although often indirectly. A quarter of the time, in real life situations, they just smile away a compliment they receive. While the DCT data and natural data are similar in the use of a majority of the semantic formulas, some differences are also found between the two types of data. Some methodological and cross-cultural implications are discussed at the end of the article.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".