Educated and Staying at Home: Asian Immigrant Wives’ Labor Force Participation in the U.S.
Bibliographic record
Abstract
An increasing number of immigrants come from Asian countries to the U.S. Little scholarly attention has been paid to the labor force participation of recent immigrant wives from these countries. By using the New Immigration Survey 2003 (Jasso, Massey, Rosenzweig, & Smith, 2005), this paper examines labor force participation of immigrant women from eastern and central Asian countries. Logistic regression results show that their own educational attainment and prior experience in professional occupation are not a significant predictor of their labor force participation, while their location of education as well as their English proficiently are found to be significant predictors. Moreover, the study found that their reason to migrate significantly influenced their likelihood of labor force participation. These results imply that unlike U.S. born women, the human capital approach does not apply in predicting labor force participation for recent Asian immigrant wives.
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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 | 0.000 |
| 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 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".