Policing, Profits, and the Rise of Immigration Detention in New York's “Chinese Jails”
Bibliographic record
Abstract
“Policing, Profits, and the Rise of Immigration Detention in New York's ‘Chinese Jails’” explains how Chinese exclusion law created a “detention economy” in upstate New York. From 1900–1909, Northern New York jails held thousands of Chinese migrants who had been apprehended by immigration authorities crossing the U.S.-Canada border, and had filed habeas corpus claims in district courts. While scholarship on Chinese Exclusion has addressed the legal battles around due process, it has overlooked the detention infrastructure that these claims produced. Because the federal immigration service had no detention facilities in the region, they “boarded out” Chinese detainees at local jails, paying counties a nightly rate for each migrant held. These contracts transformed Chinese migrants into a commodity for rural communities looking to secure federal cash, with four Northern New York counties constructing separate “Chinese Jails” in order to increase the number of Chinese migrants they could incarcerate. This article challenges the scholarship that has presented immigration detention as a Cold War era development, instead showing how communities profited off jailing migrants at the turn of the century. Through the case of U.S. v. Sing Tuck, I argue that immigration officials eventually turned to the courts to streamline deportations and reduce their need for jail space.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".