Hidden in Plain Sight: Youth Living with Complex Circumstances: An examination of the Risk Need Responsivity Rehabilitation Model’s ineffectiveness in preventing recidivism when addressing Youth Living with Complex Circumstances
Bibliographic record
Abstract
This research examines the effectiveness of the Risk-Need-Responsivity (“RNR”) theoretical framework, specifically, the Youth Service Level/Case Management Inventory Assessment Tool (“YSL/CMI”) in preventing recidivism for Youth Living with Complex Circumstances and the role it plays in decision-making and sentencing within the youth criminal justice system. To build my argument and form my conclusions for this thesis, I reviewed case law, mostly cases adjudicated in the Province of Ontario, Canada, and literature from criminology, psychology, and sociology written by scholars from Canada, other Commonwealth countries and the United States of America. I argue that the YSL/CMI is less effective or ineffective at preventing recidivism for Youth Living with Complex Circumstances than with other youth. Youth Living with Complex Circumstances (YLCC) are a distinct group of youth whose daily life experience is comprised of several static and dynamic criminogenic and non-criminogenic needs. Applying the intersectionality framework, I argue that the intersection of the criminogenic and non-criminogenic factors creates this unique group of youth who have unique needs. To increase the likelihood of preventing recidivism, these unique needs must be identified, addressed and/or targeted for services, programming or treatment. Due to the YSL/CMI’s single axis analysis approach, this risk assessment tool does not capture some of these youths’ unique and critical needs. Therefore, it is often less effective at decreasing or preventing the likelihood of recidivism for these youth. Justice Participants often rely on the risk assessment findings and recommendations when making decisions about youth who are before the court. I recommend trauma informed assessment tools become part of the YSL/CMI assessment process. I argue that both assessment tools working in tandem will improve the accuracy of assessment findings and potentially improve the appropriateness of the recommendations. These improvements should increase the likelihood that decisions made about YLCC by Justice Participants and sentences imposed by the court are better informed and better aligned with the Youth Criminal Justice Act’s Declaration of Principle and the YCJA sentencing principles. Ultimately, this should increase the likelihood of reducing or preventing recidivism for YLCC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".