Comparing the Reliability and Validity of the Criminal Sentiments Scale – Modified and the Pride in Delinquency Scale between Black and White Justice-Involved Youth
Bibliographic record
Abstract
Procriminal attitudes, key predictors of recidivism in the Risk-Need-Responsivity framework, have been shown to differ among Black and White groups (e.g., Eichelsheim et al., 2015; Mandracchia & Morgan, 2012; Wolff et al., 2013). Despite this, the reliability and validity of procriminal attitudes measures have yet to be examined cross-racially. The current study examined the reliability and validity of the Criminal Sentiments Scale – Modified (CSS-M; Shields & Simourd, 1991)) and the Pride in Delinquency Scale (PIDS; Shields & Whitehall, 1994) in a sample of 473 justice system-involved Black and White youth in Toronto, Canada. Findings indicated acceptable internal reliability (.63 α .93) for scales except the CSS-M Courts subscale in Black youth (α = .63) and the Identification with Criminal Others subscale for both groups (.55 α .68). Both measures showed acceptable convergent validity with each other (.22 < r < .69). Construct validity (as measured through confirmatory factor analyses) of both scales was acceptable for both Black and White youth, though fit indices of the CSS-M consistently fell slightly below the recommended threshold for Black youth. Both measures showed good concurrent validity for White youth and poorer, but somewhat acceptable, concurrent validity for Black youth, with fewer and weaker significant correlations between the scales and externalizing problems, aggression, and substance use problems. These findings suggest that procriminal attitudes may not reflect individual antisociality for Black youth in the way they do for White youth. The CSS-M Total and Police scores predicted recidivism for Black youth, whereas the PIDS and CSS-M subscales predicted recidivism for White youth. Results suggest that procriminal attitudes, as measured by the CSS-M and the PIDS, may reflect slightly different constructs for Black and White youth, which raises questions regarding how to use and interpret these tools in risk assessment and case management. Though the measures worked somewhat differently depending on race in this study, both have practical utility. Future research directions include examining cross-racial measurement invariance and differential item functioning of procriminal attitudes measures to better understand these constructs and scales for Black youth, teasing apart relationships between procriminal attitudes and offending for different groups (e.g., through moderations and mediations), and expanding these investigations to other racialized groups.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".