Unequal From the Start? Poverty Across Immigrant Generations of Hispanic Children
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
Recent cohorts of U.S. children increasingly consist of immigrants or the immediate descendants of immigrants, a demographic shift that has been implicated in high rates of child poverty. Analyzing data from the 2014-2018 Current Population Survey and using the U.S. Census Bureau's Supplemental Poverty Measure, we describe differences in child poverty rates across immigrant generations and assess how these disparities are rooted in generational differences in the prevalence and impact of key poverty risk factors. Our estimates show that poverty rates among Hispanic children are very high, particularly among first-generation children and second-generation children with two foreign-born parents. Low family employment is the most significant risk factor for poverty, but the prevalence of this risk varies little across immigrant generations. Differences in parental education account for the greatest share of observed intergenerational disparities in child poverty. Supplemental comparisons with third+-generation non-Hispanic White children underscore the disadvantages faced by all Hispanic children, highlighting the continued salience of race and ethnicity within the U.S. stratification system. Understanding the role of immigrant generation vis-à-vis other dimensions of inequality has significant policy implications given that America's population continues to grow more diverse along multiple social axes.
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 | 0.001 |
| Science and technology studies | 0.001 | 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