The Relationship Between Self-Esteem, Gender, Criminal Attitudes, and Recidivism in a Youth Justice Sample
Bibliographic record
Abstract
The Risk-Need-Responsivity (RNR) model deems criminal attitudes a high-priority criminogenic target for both genders while self-esteem is considered noncriminogenic, hence low priority. In contrast, self-esteem is afforded greater priority among gender-responsive researchers, while the construct of criminal attitudes is afforded lesser priority. We examined whether self-esteem and gender moderated the relationship between criminal attitudes and recidivism among 300 justice-involved youth (200 males, 100 females). Contrary to the hypothesis, high self-esteem (≥72.15th percentile) magnified the relationship between criminal attitudes (Pride in Delinquency Scale) and recidivism in females only; self-esteem levels evidenced no impact on the relationship between criminal attitudes and recidivism among males. Results suggest that prioritizing self-esteem as a treatment target among justice-involved female youth without simultaneously considering whether or not pride in criminal conduct is also present may inadvertently increase reoffending. Implications for exploring whether high self-esteem may in reality represent falsely inflated self-esteem are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".