Legal Histories as Determinants of Incorporation: Previous Undocumented Experience and Naturalization Propensities Among Immigrants in the United States
Bibliographic record
Abstract
This article examines how different histories of illegality may influence immigrants’ orientations toward acquiring citizenship in the United States. Findings from the New Immigrant Survey show that having crossed the border without authorization—compared to having no history of illegality—is associated with a higher propensity to naturalize, indicated by an expressed intention to naturalize upon eligibility and, notably, an early undertaking of the naturalization process. In contrast, there is weaker evidence that immigrants who overstayed their visas or worked without authorization differ with regards to naturalization from immigrants with no history of illegality. Results suggest that immigrants who have experienced the greatest degrees of legal insecurity in the past may be among those most likely to seek out full political membership. Thus, this article bears optimistic implications for the integration potential of previously undocumented immigrants, and highlights the importance of making available legal pathways “out of the shadows” and into the political communities of receiving states.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".