Indigenous Collaboration for Leadership Development: A Canadian Example
Bibliographic record
Abstract
The purpose of this study is to describe how a Canadian provincial government department (Alberta Sport Council) collaborated with Indigenous communities, other government inter-agencies, corporate sponsors, and private business contractors in the creation, implementation, and measurement of the impact of an Indigenous youth leadership development program (also known as the Alberta's Future Leaders program or AFL). Based on the analysis of provincial government departments reports, input by government staff in previous reports, Indigenous youth leadership development program evaluations, reviews by the youth leadership development program creators and facilitators, and university research reports on the program, a collaborative consultative process emerged. The Dynamic Facilitating Process Model evolved to systematically describe the collaborative process that took place that recognized and promoted Indigenous consultation, consent, and involvement supporting Indigenous communities' priorities and protocols. The six phases in the Dynamic Facilitating Process that emerged are (1) Defining, (2) Designing, (3) Implementing, (4) Measuring, (5) Maintaining, and (6) Refining. Each of the phases in the model is clearly described with supporting collaborative examples with the different partners. Special attention was given to measure the impact of the youth leadership development program on Indigenous youth participants and to describe the impact of the youth leadership development program in Indigenous communities. This applied research and resulting model can serve as a guide for other governments and their inter-agencies, businesses, or other organizations that wish to engage in a positive collaborative process with Indigenous communities.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.048 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".