Callous Cruelty and Blow Back: Immigration and Customs Enforcement Facilities, Riskscapes, and Community Transmission of COVID-19
Bibliographic record
Abstract
This research builds on and extends critical environmental justice research into carceral spaces. Here, the focus is on U.S. Immigration and Customs Enforcement (ICE) detention facilities in the context of the COVID-19 pandemic. Drawing on the lessons provided by the Black Lives Matter social movement and critical race theory, this research draws connections between the institutionalized racism in the criminal justice system and immigration policies. The nativist and racist rationale for harsh immigration policies asserts that callous treatment of immigrants makes U.S. society safer. However, the blow back from these policies makes U.S. society less secure and degrades the civil and political rights for all. Informed by a riskscape framework, we pursue multiscalar and empirical research into this blow back. Riskscapes encompass different viewpoints on the threat of loss across space, time, individuals, and collectives. More tangibly, in the context of the COVID-19 pandemic, ICE detention facilities provided ideal conditions for the infection to spread among the people detained, visitors, and staff. The walls and fences surrounding ICE facilities did not prevent the spread of infection to nearby communities, counties, and regions. Heightened infection rates provide tangible (and tragic) evidence of the blow back from the callousness of U.S. immigration policies in general and of ICE facilities in specific. This synthesis of critical environmental justice and riskscapes literatures lays the foundation for a textured and multi-layered understanding of the unequal and institutional dimensions of risks in and around carceral facilities.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".