Prison and Immigration Industrial Complexes: The Ethnodistillation of People of Color and Immigrants as Economic, Political, and Demographic Threats to US Hegemony
Bibliographic record
Abstract
This paper compares the sociohistorical trends that led to the development of the Prison and Immigration Industrial Complexes by demonstrating their deep roots in American public, racial, political, and penology history, and to show how these industries were used as armaments in the low intensity conflict war to keep blacks “in their place” in the post-Civil Rights Movement era, and now against Latino immigrants in a last ditch effort to preserve a dissipating white hegemonic order as the looming Browning of America unfurls. This study specifically compares the black experience in the Prison Industrial Complex, and how local policies fuel that industry, to the immigrant experience and how the Immigration Industrial Complex lucratively thrives from federal and regional antiimmigrant policies that have fueled its expansion along the border, thereby escalating the “War on Drugs” to the “War on the Border.” Scholars have argued that the Prison Industrial Complex ultimately serves to “disappear” people of color from society. I extend that contention to the Immigration Industrial Complex, by arguing that the white ruling class has benefitted the most because countless whites have escaped the wrath of these industries, which is coupled by its motivation to “purify and refine” society, more specifically to “distill” it of the “hypercriminalized” class theoretically composed of people of color in a process previously established as ethnodistillation, which have served to maintain the US’ white subjugated social order.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| 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".