Indigenizing or Appropriating? Navigating the Boundaries of Institutional Decolonization
Bibliographic record
Abstract
As Indigenous researchers and advocates, we challenge the notion of decolonizing structures rooted in colonialism. We argue that systems built and established from, and with colonialism, cannot be fully decolonized due to their colonial foundations. Authentic decolonial praxis removes and/or abolishes colonial ways of being and knowing, making way for Indigenous ways of being and knowing. Many systems that claim to be decolonizing or decolonized are instead often either indigenizing or indigenized, adding in Indigenous aspects, traditions, knowledge or culture, without consideration of colonial foundations. Sometimes this involves the inclusion of Indigenous perspectives and authentic, culturally safe Indigenous knowledges or traditions to already established practices, but requires sensitivity to risks of appropriation, where Indigenous traditions, languages, or ways of knowing are misused, stolen, and/or decontextualized. Appropriation risks engaging in performative decolonization/indigenization, without abiding by Indigenous protocols, engaging in relationality, or appropriately relaying information and knowledge with appropriate permissions. Such language use is significant within the context of institutional attempts to engage in the work of both decolonization and indigenization. Through this paper, we unpack these nuances from our perspectives as a university professor and community social worker, who work within institutions such as education, carceral, and health systems.
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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 | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.174 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.008 |
| 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".