“Crashing America’s Back Gate”: Illegal Europeans, Policing, and Welfare in Industrial Detroit, 1921-1939
Bibliographic record
Abstract
Between 1921 and 1939, the border separating Detroit, Michigan, from Windsor, Canada, represented a key site for undocumented immigration on America’s northern border, and the migrants in question were European. This essay examines industrial urban America in the wake of 1921 and 1924 Immigration Acts to reveal the effects of restriction and policing on America’s emerging welfare state. It finds that in Detroit, after federal policies gave nativism the force of the law, local smuggling, policing, and enforcement practices branded foreign-born Europeans as illegal regardless of their legal status. During the New Deal Era, when the federal government built America’s welfare system, the stakes for belonging to the nation-state became higher than ever. In this moment of transition, local actors drew on rhetoric connecting foreigners to crime and dependence to urge federal policymakers to tie welfare benefits to citizenship. These local initiatives in Detroit and across the nation prompted the federal government to purge non-citizens from the Works Progress Administration, the new welfare program most associated with dependence and relief. Ultimately, this essay argues that a shift in national mood about foreignness in urban America took hold of the United States in the 1920s and shaped federal welfare policy by the 1930s.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".