How Investing in At-Risk Youth Today Will Save Albertans Money in the Future
Bibliographic record
Abstract
The choice to use custodial sentencing as the dominant punishment solution, or to invest in the development and implementation of juvenile diversion programs aimed at addressing the root causes of crime, has remained a point of contention between decision makers in Canada.1 Seemingly contradictory behaviour between the Canadian Federal Government and the Calgary Police Service lead me to conduct an inquiry into the effectiveness of existing methods of intervention for youth crime in society. An extensive literature review and cost analysis revealed that recent legislation promoting the use of incarceration is not likely to generate desirable social or financial benefits for Canada. Rather, methods of early intervention and multi-systemic therapy should be pursued as a means to reduce crime in society, and incarceration should be utilized only as a last resort. Although there is a convincing body of discernable evidence in favour of preventative and circumstantial reactive methods in the community, the investment in such programs can be a hard political sell. Using a cost analysis and a rough calculation of Alberta’s Willingness To Pay (WTP) to reduce crime, I am able to conclude that the implementation of juvenile diversion programming should not be viewed as politically risky, but instead should be viewed as being politically advantageous.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".