Policing the Migrant “Crisis”: How U.S. Immigration Policy Criminalizes and Racializes Black Migrants at the U.S./Caribbean Border
Bibliographic record
Abstract
Borders and bordering practices reveal how migrants become racialized and divided into binaries of Us/Them, native/foreign, citizen/noncitizen, or legal/illegal. In the United States, much of this research centers around the U.S./Mexico border, and to a lesser extent the U.S./Canada border. However, less attention is paid to the watery, “third” border separating the United States from the Caribbean and Latin America. I draw on examples from Afro-descent Haitian, Cuban, Jamaican, and Dominican diasporas to show how bordering practices target and criminalize Black migrants through the rhetoric of “crisis,” “threat,” and racialized “crimmigration.” Together, the U.S./Caribbean border and the policies generated from it externalize the reach of U.S. border practices into the Caribbean. Like the U.S./Mexico border, it is a racialized space where bordering practices mediate, reinforce, and reimagine race, nation, legality, and citizenship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".