Development and Validation of a Surname List to Define Chinese Ethnicity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it