Development and Validation of a Surname List to Define Chinese Ethnicity
Bibliographic record
Abstract
OBJECTIVE: Surnames have the potential to accurately identify ancestral origins as they are passed on from generation to generation. In this study, we developed and validated a Chinese surname list to define Chinese ethnicity. METHODS: We conducted a literature review, a panel review, and a telephone survey in a randomly selected sample from a Canadian city in 2003 to develop a Chinese surname list. The list was then validated to data from the Canadian Community Health Survey. Both surveys collected information on self-reported ethnicity and surname. RESULTS: Of the 112,452 people analyzed in the Canadian Community Health Survey, 1.6% were self-reported as Chinese. This was similar to the 1.5% identified by the surname list. Compared with self-reported Chinese ethnicity (reference standard), the surname list had 77.7% sensitivity, 80.5% positive predictive value, 99.7% specificity, and 99.6% negative predictive value. When stratifying by sex and marital status, the positive predictive value was 78.9% for married women and 83.6% for never married women. CONCLUSIONS: The Chinese surname list appears to be valid in identifying Chinese ethnicity. The validity may depend on the geographic origins and Chinese dialects in given populations.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".