Does “Jamal” receive a harsher sentence than “James”? First-name bias in the criminal sentencing of Black men.
Bibliographic record
Abstract
OBJECTIVE: Using archival and experimental methods, we tested the role that racial associations of first names play in criminal sentencing. HYPOTHESES: We hypothesized that Black defendants with more stereotypically Black names (e.g., Jamal) would receive more punitive sentences than Black defendants with more stereotypically White names (e.g., James). METHOD: In an archival study, we obtained a random sample of 296 real-world records of Black male prison inmates in Florida and asked participants to rate the extent to which each inmate's first name was stereotypically Black or stereotypically White. We then tested the extent to which racial stereotypicality was associated with sentence length, controlling for relevant legal features of each case (e.g., criminal record, severity of convicted offenses). In a follow-up experiment, participant judges assigned sentences in cases in which the Black male defendant was randomly assigned a more stereotypically Black or White name from our archival study. RESULTS: Controlling for a wide array of factors-including criminal record-we found that inmates with more stereotypically Black versus White first names received longer sentences β = 0.09, 95% confidence interval (95% CI) [0.01, 0.16]: 409 days longer for names 1 standard deviation above versus below the mean on racial stereotypicality. In our experiment, participant judges recommended significantly longer sentences to Black inmates with more stereotypically Black names above and beyond the severity of the charges or their criminal history, β = 0.07, 95% CI [0.02, 0.13]. CONCLUSIONS: Our results identify how racial associations with first names can bias consequential sentencing decisions despite the impartial aims of the legal system. More broadly, our findings illustrate how racial biases manifest in distinctions made among members of historically marginalized groups, not just between members of different groups. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".