Socioeconomic marginality in sentencing: The built-in bias in risk assessment tools and the reproduction of social inequality
Bibliographic record
Abstract
This article develops a sociological analysis and critique of including socioeconomic factors such as education, employment, income and housing in risk assessment tools that inform sentencing decisions. In widely used risk assessment tools such as the Level of Service Inventory-Revised (LSI-R) (Canada, US), the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) (US), the Offender Assessment System (OASys) (UK) and the Recidive InschattingsSchalen (RISc) (the Netherlands), socioeconomic marginality contributes to a higher risk score, which increases the likelihood of a (longer) custodial sentence for underprivileged offenders compared to their more privileged counterparts. While this has been problematized in relation to gender and racial/ethnic bias, the problem of socioeconomic bias in itself has received little attention. Given the already marginalized position of many justice involved individuals and longstanding concerns about such disparities, and the adverse effects of imprisonment on socioeconomic opportunities, it is essential to evaluate the unintended social consequences of assessing socioeconomic marginality as ‘risk factor’. Elaborating on earlier critiques, I conceptualize risk-based sentencing as a meaning-making process through which (access to) resources and recognition are distributed among offender populations. Through tracing in detail two cultural processes – stigmatization and rationalization – I analyse how risk assessment is likely to produce sentencing disparities as well as to reproduce, and possibly exacerbate, social inequalities more generally.
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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 | 0.039 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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".