Decolonizing both researcher and research and its effectiveness in Indigenous research
Bibliographic record
Abstract
How does one decolonize and reclaim the meanings of research and researcher, particularly in the context of Western research? Indigenous communities have long experienced oppression by Western researchers. Is it possible to build a collaborative research knowledge that is culturally appropriate, respectful, honoring, and careful of the Indigenous community? What are the challenges in Western research, researchers, and Western university methodology research training? How have ‘studies’ – critical anti-racist theory and practice, cross-cultural research methodology, critical perspectives on environmental justice, and land-based education – been incorporated into the university to disallow dissent? What can be done against this disallowance? According to Eve Tuck and K Wayne Yang’s (2012) suggestion, this article did not use the concept of decolonization as a substitute for ‘human rights’ or ‘social justice’, but as a demand of an Indigenous framework and a centering of Indigenous land, Indigenous sovereignty and Indigenous ways of thinking. This article discusses why both research and researcher increasingly require decolonization so that research can create a positive impact on the participants’ community, and conduct research ethically. This article is my personal decolonization and reclaiming story from 15 years of teaching, research and service activities with various Indigenous communities in various parts of the world. It presents a number of case studies of an intervention research project to exemplify the challenges in Western research training, and how decolonizing research training attempts to not only reclaim participants’ rights in the research but also to empower the researcher. I conclude by arguing that decolonizing research training creates more empathetic educators and researchers, transforming us for participants, and demonstrating how we can take responsibility for our research.
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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 | 0.564 | 0.569 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.027 | 0.270 |
| Scholarly communication | 0.039 | 0.051 |
| Open science | 0.006 | 0.061 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".