You can’t just bring people here and then not feed them: A case in support of Indigenous-led training environments
Bibliographic record
Abstract
By and large, academic research in geography has advanced the colonial project, and been synonymous with extractive and reductionist research practices that subjugate Indigenous people. To counteract these harmful impacts and produce research that supports the needs of communities, advancing Indigenous sovereignty over research is vital. By presenting a case study of an Indigenous research space at a Canadian University, we argue that Indigenous training environments are more than a shared, physical space; they provide essential emotive and relational spaces of collaborative learning, wherein trainees practice relationship-building, reciprocity, and accountability. This article argues that decolonizing academic spaces dedicated to Indigenous geographic research will be essential to meeting the ethical imperative of Indigenous control over knowledge production. There is a current deficit of culturally appropriate spaces that support both the whole person and their learning. We highlight the impact of Indigenous training environments in nurturing respectful, long-standing relationships with peers, community, and research partners; a critical element of Indigenous geographies, yet one of the most challenging aspects of upholding meaningful and decolonizing research. By drawing on our diverse perspectives and research projects, we reflect on how an Indigenous-led training environment, rooted in Indigenous ways of knowing, can contribute to relational accountability both within and outside of these spaces. As more communities assert their authority over these processes, the need for respectful research grows, and it is anticipated that this article will provide a useful guide and support for emerging Indigenous training environments.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.068 | 0.031 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.010 | 0.014 |
| 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".