A review of collaborative research practices with Indigenous Peoples in engineering, energy, and infrastructure development in Canada
Bibliographic record
Abstract
Abstract Background Indigenous Peoples in Canada have survived hundreds of years of colonization and systematic exploitation, including actions carried out in the pursuit of energy resources and infrastructure development in traditional Indigenous territories. Research has been a tool in this exploitation through its legacy of research ‘on’ rather than ‘with’ Indigenous Peoples. As societies grapple with reconciliation, including how to build partnerships for sustainable land and energy development, engineering and technical research must use respectful approaches that centre on Indigenous Peoples and Indigenous Knowledge Systems. Main text This preliminary review aims to be a step to address the lack of literature on respectful research with Indigenous Peoples within the context of engineering, energy, and infrastructure. To this end, we: (a) summarize three key frameworks that have been used in technical research projects for carrying out research respectfully, as defined by Indigenous and Indigenist ways of knowing and doing (Research is Ceremony, Two-Eyed Seeing, and doing research in a “Good Way”) and derive from them overarching principles; (b) identify a sample of 13 engineering, energy and infrastructure research projects that report using an Indigenous-centred approach. These relate to five technical areas, whose relevance to Indigenous communities was verified through community partners: water, energy, housing, telecommunications, and food systems; (c) assess the extent to which these 13 projects applied the principles of respectful research when working with Indigenous communities. Among the 13 projects identified, it is evident that some researchers in the fields of engineering, energy, and infrastructure are struggling and striving to engage respectfully with Indigenous communities. However, few include full details of their relationships and interactions with Indigenous communities in their published work. Conclusions These findings suggest a lack of details on respectful collaboration with Indigenous communities in technical literature. Gaps include a scarcity of evidence that Indigenous communities were involved in high-level decision-making or provided post-project feedback. Further work is needed to embed respectful research principles into the training, processes, and institutions of technical fields. This is essential to ensure ethical partnerships between technical researchers and Indigenous communities.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".