Working Across Contexts: Practical Considerations of Doing Indigenist/Anti-Colonial Research
Bibliographic record
Abstract
Although Indigenous scholars have been documenting Indigenous research methodologies, little has been written on the practical considerations of doing research across Indigenous/Settler contexts. As a small social work research team (two Cree researchers and one Settler) exploring Indigenous aging, our work crossed several contexts: academic and community, social locations within the team, and epistemes. Centering the research on an Indigenist, anti-colonial framework allowed us to highlight and correct for colonial power dynamics throughout the project. By enacting Indigenism together, we found that Indigenous and Settler researchers can create a space of deep learning and knowledge co-creation with communities. However, this work was challenging, risky, and at times difficult. Learning to navigate some of these complexities required ongoing attention to our relational accountabilities. We detail lessons learned from each of our perspectives, concluding with implications, community obligations, and directions for future research.
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 | 0.340 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.054 | 0.125 |
| Scholarly communication | 0.032 | 0.039 |
| Open science | 0.008 | 0.030 |
| Research integrity | 0.011 | 0.015 |
| 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".