Exploring the Relationship Between Childhood Adversity, Self-Worth, and Criminal Identification in a Mixed Gender Sample of Adolescent Offenders
Bibliographic record
Abstract
Research regarding the impact that childhood adversity and criminal identification (criminal associates and criminal attitudes) has on self-worth, and how that relationship may ultimately lead to criminal activity differently in justice involved female youth versus justice involved male youth is scarce (Bonta & Andrews, 2017;Van Voorhis, 2012).This study examined (1) if childhood adversity influences self-worth, which in turn leads to recidivism and higher levels of self-reported aggression and (2) whether self-worth strengthens or weakens the relationship between criminal identification and recidivism.Archival data involving 312 justice involved youths from Ontario found that self-worth did not mediate the relationship between childhood adversity and recidivism or self-reported aggression.However, an interesting three-way interaction emerged between gender, self-worth and criminal attitudes.Specifically, while self-worth buffered the relationship between criminal attitudes and recidivism among females, self-worth magnified the relationship between criminal attitudes and recidivism among males.As suggested by gender responsive scholars, positive self-worth appears to be an important treatment target for females that can buffer the risk to re-offend but as suggested by gender neutral scholars may serve to inflate risk among justice involved male youth who also evidence criminal attitudes.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".