Behind the Scenes of Youth-led Community Events: A Participatory Evaluation Approach Using Photovoice in a Canadian First Nation Community.
Bibliographic record
Abstract
Abstract Challenges created by colonial policies have created a range of negative impacts on the health and well-being of First Nations, Mtis and Inuit (FNMI) youth. Research examining leadership programming for FNMI youth has begun to demonstrate positive impacts. Participatory evaluation aligns with the philosophy of leadership programming and has the potential to promote multiple benefits for the youth and community within FNMI context. The purpose of this research was to implement a participatory evaluation examining youth-led community events within a leadership program for FNMI youth. The data was collected using methods based on youth participatory evaluation principles and Photovoice practices including capacity building, stakeholder analysis, photo exploration and utilization-focused activities. A thematic analysis was used to examine the data and five themes were identified: (a) fun and fulfilling to engage the children, (b) positive outcomes for youth leaders, (c) community impacts, (d) challenges and (e) opportunities for improvement. This study applies a novel approach that combines Youth Participatory Evaluation and Photovoice to examine a youth leadership program for FNMI youth. The procedures and the results regarding program impacts and implementation issues are relevant for research and evaluation in programming for FNMI youth, as well other youth-led programs. Keywords: Leadership; Youth engagement; Positive youth development; Program evaluation; Indigenous; Aboriginal
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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 | 0.041 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".