Reconciling Positionality: An Indigenous Researcher’s Reflexive Account
Bibliographic record
Abstract
As researchers, we take the subjectivity we have formed over time into each research project. These subjective traces are a product of our lived experiences, gradually shaping our perceptions and interpretations of the world. Despite being an Indigenous scholar, my lived experience has not primarily occurred within Indigenous settings, resulting in biased subjectivities emerging while researching First Nations communities. This paper describes my subjective traces and reflects on the biases I uncovered while researching Indigenous communities. The reflection consists of three main sections: a personal background, a description of experiences in the research sites, and a discussion of what the reflections mean to the decolonization of academia. Overall, I hope that the insights in this reflection go beyond the mere recognition of Indigenous voices and encourage Indigenous researcher activism toward advancing and diversifying academia.
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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 | 0.117 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.023 | 0.093 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.007 | 0.015 |
| 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".