Engaging Northern Aboriginal Youth Key to Sustainable Development
Bibliographic record
Abstract
This paper argues that governments, industry, and educational institutions need to engage better with Aboriginal communities when it comes to training and economic development initiatives in Northern Saskatchewan. Building accessible programs and engaging with youth are keys to sustainable development. The social capital in northern communities is resilient and embedded in kinship networks, but with the fast-growing youth population, increased collaborative engagement between communities, governments, training institutions, and industry is required to help build relevant programs for youth. Findings from the 2009–2012 Northern Aboriginal Political Engagement study suggest that, given the opportunity and proper incentives, northern Aboriginal youth want to and will engage in the development of themselves and their communities. Most youth believe that priority should also be given to address problems with addictions (alcohol, drugs). At a minimum, these findings imply that better coordination is needed between health programs (mental health and addiction) and training and economic development programs for youth. This paper is part of a special collection of brief discussion papers presented at the 2014 Walleye Seminar held in Northern Saskatchewan, which explored consultation and engagement with northern communities and stakeholders in resource development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".