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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.071 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.049 | 0.074 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.006 | 0.039 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".