SETTLER FRAGILITY: Four Paradoxes of Decolonizing Research
Bibliographic record
Abstract
This dialogic autoethnography, in which the authors reflect on their experiences as settlers who have researched with Indigenous communities, maps four paradoxes settler researchers need to negotiate in decolonizing research. The term settler fragility signals a settler positioning of innocence in colonization, which simultaneously recenters colonial power to secure settler futures. In research, settler fragility must be confronted through four paradoxes: (1) the paradox of learning Indigenous worldviews in a profound way but without appropriation; (2) the paradox of unsettling research by undoing colonial epistemologies in which settlers problematically aim to feel settled; (3) the paradox of reconciling research to improve relationships with Indigenous communities which can lead to reconciling settlers with their place in colonialism, rather than with Indigenous research partners; and (4) the paradox of decolonizing research in which settler research in colonial universities is recognized as incommensurate with decolonization and yet must be undertaken to decolonize the university. Contributing a tentative set of settler research practices, this paper aims to expand dialogues about how settlers can overcome settler fragility through negotiating the four paradoxes of decolonizing research to develop authentic relationships with Indigenous communities, researchers, and research partners.
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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 | 0.051 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.083 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".