Collateral Consequences of Criminal Convictions: Confronting Issues of Race and Dignity
Bibliographic record
Abstract
This article explores the racial dimensions of the various collateral consequences that attach to criminal convictions in the United States. The consequences include ineligibility for public and government-assisted housing, public benefits and various forms of employment, as well as civic exclusions such as ineligibility for jury service and felon disenfranchisement. To test its hypothesis that these penalties, both historically and contemporarily, are rooted in race, the article looks to England and Wales, Canada and South Africa. These countries have criminal justice systems similar to the United States’, have been influenced significantly by United States’ criminal justice practices in recent years, have turned to increasingly punitive punishment schemes and have histories of disproportionately incarcerating people of color. This article is the first that offers a detailed comparative examination of collateral consequences. The examination finds that the consequences in the United States are harsher and more pervasive than those in these other countries. It also shows that Canada and South Africa have articulated broad dignity protections for incarcerated and formerly incarcerated individuals that are influenced by human rights notions of rights and privileges. Canada, in particular, has employed mechanisms to ease racial disparities in incarceration. Drawing lessons from these countries, the article offers steps to ease the legal burdens placed on individuals with criminal records in the United States, as well as to lessen the disproportionate impact these post-sentence consequences have on individuals and communities of color.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".