Bibliographic record
Abstract
The well-being of Indigenous peoples in Canada has been impacted by the historical interactions between the federal government and Indigenous communities. There is currently an over representation of Indigenous peoples in the justice system and a lack of police services meeting the cultural needs of First Nations communities. The Canadian government has instituted a program to assist in the appropriate delivery of police services to Indigenous communities through the First Nations Policing Program (FNPP). The purpose of this research is to explore how federal policing authorities make decisions about Indigenous policing, specifically the FNPP. Various methods of research were used, such as searching through publicly available federal policy documents and data. These resources were acquired by requesting information through the Access to Information and Privacy Act. The findings of this research demonstrate that the FNPP attempts to undertake consultations for the development of appropriate policies for First Nations communities. However, this consultation can be undermined by groupthink in small communities. Consultations might be improved using the Delphi principle, a method that assists in developing suitable policies for policing. The relevance of this discussion extends beyond the important issue of Indigenous over-representation in the justice system, also addressing the need for effective community policing for the unique circumstances of each community. Balancing community-focused expert advice using the Delphi method, and considering the risk of groupthink, consultation processes may allow individual communities to move towards effective policing using the FNPP.
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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 | 0.043 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".