Crimmigrating Narratives: Examining Third-Party Observations of US Detained Immigration Court
Bibliographic record
Abstract
Examining what we call "crimmigrating narratives," we show that US immigration court criminalizes non-citizens, cements forms of social control, and dispenses punishment in a non-punitive legal setting. Building on theories of crimmigration and a sociology of narrative, we code, categorize, and describe third-party observations of detained immigration court hearings conducted in Fort Snelling, Minnesota, from July 2018 to June 2019. We identify and investigate structural factors of three key crimmigrating narratives in the courtroom: one based on threats (stories of the non-citizen's criminal history and perceived danger to society), a second involving deservingness (stories of the non-citizen's social ties, hardship, and belonging in the United States), and a third pertaining to their status as "impossible subjects" (stories rendering non-citizens "illegal," categorically excludable, and contradictory to the law). Findings demonstrate that the courts' prioritization of these three narratives disconnects detainees from their own socially organized experience and prevents them from fully engaging in the immigration court process. In closing, we discuss the potential implications of crimmigrating narratives for the US immigration legal system and non-citizen status.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".