Who gets a second chance? Compliance, classification, and criminal conviction
Bibliographic record
Abstract
Abstract Felony conviction carries lifelong consequences that impact civic, economic, and social rights and opportunities, yet not everyone who is found guilty of a felony will bear the mark of conviction. Deferred adjudication is an increasingly popular intervention that offers legally guilty defendants protection from the mark of conviction conditional on the completion of community supervision. By conditioning conviction on discretionary assessments of compliance, rather than legal establishment of guilt, deferral and similar interventions may exacerbate inequality and further concentrate the mark of conviction among marginalized groups. However, relatively few studies examine disparities in the decision to defer conviction and dismiss charges. In this study, we draw on twenty years of court records to ask “for whom is the mark of conviction and formal punishment dependent on compliance rather than the legal establishment of guilt, and who passes the test of compliance?” Findings reveal that, even when accounting for features of the offense, both race and socioeconomic status condition who gets a “second chance” at a clean record. These findings have implications for how we study inequality in criminal courts and understand the production and meaning of conviction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".