Finding our ‘good way’: critical reflections on researching disability, connection, and community from an Indigenous perspective
Bibliographic record
Abstract
From our standpoints as Indigenous and ally researchers in the social sciences and socio-legal field, we offer an autoethnographic, reflexive account of a three-year research collaboration about close relationships, disability, and social connection. After engaging in one structured reflection exercise, several informal reflective conversations, and a pipe ceremony that marked the beginning of our next research endeavour together, we outline three emergent issues, each representing a point of struggle that ultimately became a strength in our research approach and contributions to knowledge. We also put forward a fourth issue that emerged in light of the COVID-19 pandemic and that has deeply shaped our research and reflection since. Our paper is intended to function as both a specific examination of lessons learned and knowledge generated in our experience of community-engaged Indigenous research, and also as a broader ensemble of principles that can be useful for doing thoughtful and critical reflective practice from an Indigenous perspective. We maintain that, taken together, the issues and principles we bring forth help to clarify how reflection on Indigenous research can assess whether and how we are doing our work in a ‘good way,’ and exemplify an approach that strives to empower communities while generating new knowledge.
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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 | 0.073 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.048 | 0.115 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.012 | 0.034 |
| 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".