Effectively Engaging with Indigenous Communities through Multi-Methods Qualitative Data Collection and an Engaged Communications Plan
Bibliographic record
Abstract
A research project on social and economic capacity building through Aboriginal entrepreneurship employed a highly engaged approach with communities in northern Saskatchewan, Canada. The involved communities were viewed as research partners, and the research team applied a comprehensive communications plan to provide community members with relevant and timely information about the project and summaries of its outcomes as those results emerged. The study was designed to empower those who traditionally had been viewed as participants on whom research could be conducted, and ensure that the research was instead conducted with and for them. This research project encouraged youth and adults to express their perspectives in new and engaging ways that gave them the opportunity to more meaningfully have their voices heard. One important outcome from engaging more with communities was that research team members felt more engaged with their own project.
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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 | 0.159 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".