‘I thought people would be mean and shout.’ Introducing the Hobbema Community Cadet Corps: a response to youth gang involvement?
Bibliographic record
Abstract
Hobbema, Alberta, Canada is a community comprised of four First Nations. As with many of Canada's Aboriginal communities, Hobbema's population is young. High rates of socio-economic disadvantage, violence, family dysfunction, and substance abuse are linked to colonization, residential school policies, and discrimination. Crime rates, including gang-related crime in the area, are disproportionately high. In 2005 two police officers created the Hobbema Community Cadet Corps, a program which offers youth a pro-social alternative to criminal activity and gang involvement. The program provides youth with the opportunity to learn the value of group identity, discipline, and camaraderie. It also provides opportunities for recreational activities and travel otherwise unavailable to many in this impoverished area. This paper provides a description of the HCCCP, an overview of its activities and structure, and situates it within the crime prevention through social development framework which emphasizes the importance of building protective factors and reducing risk factors surrounding youth. Finally, a preliminary evaluation which highlights some of the challenges faced by program instructors is offered. The authors caution against relying solely on individualizing imperatives in attempts to deal with social structural issues of the types faced by citizens in Hobbema.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".