Callous Cruelty and Blow Back: Immigration and Customs Enforcement Facilities, Riskscapes, and Community Transmission of COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it