Schooling for Newcomers: Variation in Educational Persistence in the Northern United States in 1920
Bibliographic record
Abstract
Early in the 20th century, high rates of international migration from Europe and an increasing number of migrants from the South were rapidly changing the composition of cities in the northern United States. Within this dynamic environment, families faced a more complex set of decisions for the preferred economic roles of their members. For adolescents, families chose between the immediate economic rewards of sending them into the workforce and deferring benefits by extending their educational careers. This article uses the 1920 Public Use Microdata Sample to examine racial and ethnic variation in school enrollment for adolescents aged 14 to 18. It proposes a conceptual model that uses a variety of social, economic, and cultural forces to anticipate differences in schooling between international immigrants and domestic migrants, as well as across generations of both groups. The statistical analyses reveal large racial and ethnic differences in schooling for both boys and girls. The most surprising finding is for second-generation black female migrants from the South, who were significantly more likely than were all other groups of female adolescents to be enrolled in school. The authors speculate that this result is due to a combination of “immigrant optimism” and restricted employment opportunities for second-generation black female migrants in the North.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".