Settler allies are made, not self-proclaimed: Unsettling conversations for non-Indigenous researchers and educators involved in Indigenous health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it