You can’t just bring people here and then not feed them: A case in support of Indigenous-led training environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it