Self-Location and Ethical Space in Wellness Research
Bibliographic record
Abstract
Working with Indigenous communities involves responsibility, relationship, respect, and reciprocity (Kirkness & Barnhardt, 2016). Our research consists of a partnership with Nipissing First Nation to explore their citizens’ understanding of wellness. Our aim is to tell a collective story of wellness based on the experiences of Nipissing First Nation citizens. As part of our relational process, our research team engaged in an exercise of self-location in preparation for working with Nipissing First Nation stories. This process involved looking back into our own stories of wellness from three temporal points: as children, youth, and adults. Our collective perspective of wellness involved three main themes of relationship, identity, and determinants of health. This exercise helped researchers become aware of their own subjective lenses about wellness. Awakening to our own stories helped us to recognize the ethical space that existed between us as researchers, the stories we will gather, and the perspectives of our community advisory committee. Engaging in this exercise illuminated the need for a continual reflexive stance, consistently being mindful about the privilege we hold as researchers and the invisible stories that creep into an analysis. The process of self-location was an essential element in beginning our research journey. It prepared us for working respectfully and reciprocally with the community that honours the ethical space we collectively share.
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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 | 0.109 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.024 | 0.101 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".