Understanding settler moves to innocence: A co-generative inquiry on personal complicities in settler colonialism towards acts of refusal
Bibliographic record
Abstract
Indigenous scholars consistently recommend that non-Indigenous settlers learn more about their implications in settler colonialism, a complex system that privileges settlers through the erasures of Indigenous peoples and their claims to land and sovereignty. Tuck and Yang’s (2012) article Decolonization Is Not a Metaphor challenges readers to unpack personal actions that assuage settler guilt, cautioning that without doing so, decolonization can easily slide into metaphorical meaninglessness without true political or economic consequences. The aim of this paper is to show the ways in which two settler educator-researchers examine their own lived experiences, through their whiteness, femininity, religious affiliations, career paths, and privileges. We develop narratives about the ways in which our settler positionalities, both as social science researchers and teachers, are mired in settler colonial logics that are not easily brought to a halt. Our research has implications in relation to decolonizing theory, particularly around the discourse of allyship, as we examine the ways in which settler researchers may (or not) relinquish whiteness through acts of refusal.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.067 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| 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".