Do Sub-Cultural Norms Survive Immigration?—Cantonese and Mandarin Fertility in the United States
Bibliographic record
Abstract
This paper analyzes Public-Use Microdata Samples (PUMS) data from the 2000 US Census to ascertain whether evidence exists that subcultural norms survive immigration transitions to affect the fertility of the two largest Chinese immigrant subgroups, Cantonese and Mandarins, in the United States. The results indicate that the Cantonese, who are believed to be more pronatalistic and have higher fertility than the Mandarins in China, continue to exhibit these tendencies in the United States. A significant portion of the fertility disparity between the two groups can be explained by differences in migration experiences, demographic characteristics and socioeconomic status. Higher Cantonese fertility, however, persists even when all these factors are controlled, suggesting a lingering effect of pronatal subcultural norms. Furthermore, levels of education and the degree of assimilation, which play important roles in depressing fertility and also in explaining some of the groups’ difference in fertility, are also associated with these groups’ cultures as both cause and effect. Some possible causes of Cantonese pronatal norms and their persistence in the United States are explored as well.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".