Dynamic Nature of Positionality: How my Positionality has Changed Through Experience
Bibliographic record
Abstract
As an individual of mixed Métis and European descent, growing up and working/studying in a colonial system, I often reflect on how my biases, perspectives, and identity impact my research in Indigenous health promotion. I am aware that my position of privilege may unintentionally marginalize and disempower others. However, I also acknowledge that my connection to my Métis family and community has given me insights into the perceptions and worldview of Indigenous communities in Canada. In this paper, I explore the dual nature of my position and how it is challenged and shaped through my work with the Kahnawà:ke School Diabetes Prevention Program, Indigenous health promotion leaders, and interactions with Indigenous community members. I discuss how my cultural and personal backgrounds, influenced by my Métis and European heritage, are both questioned and reinforced in these experiences, leading to a deeper connection with my family and community. By considering my positionality, I discuss how it both contributes and poses challenges to the research process in Indigenous health promotion contexts.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.038 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".