White Privilege and the Decolonization Work Needed in Evaluation to Support Indigenous Sovereignty and Self-Determination
Bibliographic record
Abstract
Abstract: This paper builds on a keynote paper presented at the 2018 Canadian Evaluation Society annual conference by Kate McKegg, a Pākehā, non-Indigenous evaluator from Aotearoa, New Zealand. Kate reflects on the concept and implications for Indigenous people of white privilege in colonized Western nations. She discusses some of the ways in which white privilege and its consequences play out in the field of evaluation, perpetuating colonial sentiments and practices that maintain and reinforce inequities and injustice and potentially threaten the social justice aspirations of the field. Kate argues that those with white privilege have much work to do, unpacking and understanding their privilege if they are to have any chance of playing a role in deconstructing and dismantling the power structures that hold colonizing systems in place. She suggests that for evaluators to be effective allies for Indigenous sovereignty and self-determination, they must undertake ideological, cultural, emotional, and constitutional work. This work will be tough and scary and is not for the faint hearted. But it is vital to unlocking the potential transformation that can come from just and peaceful relationships that affirm and validate Indigenous peoples' ways of knowing and being.
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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 | 0.125 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.038 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".