Finding the Lost Generation: Identifying Second-Generation Immigrants in Federal Statistics
Bibliographic record
Abstract
This article underscores the importance of adding a question on parental birthplace to the American Community Survey (ACS). This question was removed from the long form of the U.S. Census after 1970 and replaced by a question on ancestry. While the former provides accurate information about a demographic fact that is critical to the identification of the children of immigrants, the latter refers to a subjective social construction that has limited utility for purposes of program administration, apportionment, or governance. At the time that the parental birthplace question was eliminated, the percentage of ACS respondents who were foreign-born had reached an all-time low, and the second generation was aging and shrinking, so the loss to the nation’s statistical system was not immediately apparent. With the revival of immigration in the final quarter of the twentieth century, the inability to identify and study the second generation has become glaringly apparent. Immigrants and their children now constitute a quarter of the U.S. population: their nonwhite racial origins and a widespread lack of legal documents among them render their prospects for integration uncertain. Our current inability to accurately measure progress between first- and second-generation immigrants now constitutes a major weakness in the U.S. statistical system.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".