Settler allies are made, not self-proclaimed: Unsettling conversations for non-Indigenous researchers and educators involved in Indigenous health
Bibliographic record
Abstract
Background: While many settler allies are eager to help towards the goal of disrupting racism, a clearer understanding of how best to harness this eagerness is required within the field of Indigenous health, a field currently comprised mainly non-Indigenous scholars, researchers and educators. Purpose: Responding to this challenge, this article aims to identify ways of working towards disrupting settler colonialism and addressing racism in all of its manifestations by building settler allyship and adopting an anti-racist lens within the field of Indigenous health. The article describes how to approach building settler allyship by implementing anti-racist acts. Method: By using anti-racist scholarship and showcasing recent public examples of anti-Indigenous racism, the author describes how settler allies can approach developing unsettled, critical and anti-racist conversations with one another and in respectful ways with Indigenous peoples. As many Indigenous peoples continue to identify ongoing racism, there is a need for informed, unsettled, anti-racist allies willing to challenge their own complicity to then take action when anti-Indigenous racism occurs. Actions include critical self-reflection, confronting white supremacy and implementing demonstrably anti-racist acts. Conclusion: Findings provide the basis for amplifying unsettling conversations between engaged settler allies to develop anti-racist ways of fostering and extending relationships with Indigenous people and scholars.
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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 | 0.039 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.040 | 0.040 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".