Pursuing Mutually Beneficial Research: Insights from the Poverty Action Research Project
Bibliographic record
Abstract
Research with, in, and for First Nations communities is often carried out in a complex environment. Now in its fourth year, the Poverty Action Research Project (PARP) has learned first-hand the nature of some of these complexities and how to approach and work through various situations honouring the Indigenous research principles of respect, responsibility, reciprocity, and relevance (Kirkness & Barnhardt, 2001). By sharing stories from the field, this article explores the overarching theme of how the worlds of academe and First Nations communities differ, affecting the research project in terms of pace, pressures, capacity, and information technology. How PARP research teams have worked with these challenges, acknowledging the resilience and dedication of the First Nations that are a part of the project, provides insights for future researchers seeking to engage in work with Indigenous communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.918 | 0.528 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.962 | 0.003 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.001 | 0.747 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".