Bibliographic record
Abstract
Decolonizing methodologies are gaining increasing prominence in diverse research contexts in which Indigenous peoples are researchers, research partners, participants, and knowledge users. As political and intellectual allies committed to actively resisting and redressing the colonizing potential of research and advancing social change, non-Indigenous scholars are also enacting decolonizing methodologies. By drawing on the author’s experiences as a non-Indigenous researcher partnering with an Indigenous early childhood program in Canada, this article illustrates the interconnected ways in which relationality provides the necessary epistemological scaffolding to actualize the underlying motives, concerns, and principles that characterize decolonizing methodologies. Relationality draws attention to the multiple intersecting influences that shape research and knowledge itself, emphasizes reciprocity, and is compatible with many Indigenous worldviews. This article contributes toward the ongoing international dialogue about decolonizing methodologies and is directed primarily to non-Indigenous researchers and graduate students who are questioning how to “do” community-based decolonizing research involving Indigenous peoples.
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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 | 0.038 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.082 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".