Engineering research with Indigenous communities in Canada: a systematic review of housing, infrastructure, and sustainability
Bibliographic record
Abstract
This literature review explores engineering research in Canada that engages Indigenous Peoples, focusing on housing, infrastructure, and sustainability. Of 87 studies reviewed, only nine were published in engineering journals, revealing a significant disciplinary gap. Most research stems from public health, emphasizing the consequences of inadequate housing—such as mold, overcrowding, and poor ventilation—on community health and well-being. The review highlights the need for culturally relevant, high-performance building solutions that integrate Indigenous knowledge and engineering expertise. Key priorities include appropriate HVAC systems, moisture-resistant materials, energy-efficient design, scalable solutions, and renewable energy suited to local conditions. Civil and mechanical engineering, building science, and materials engineering can play critical roles in addressing these needs. This paper calls for a shift toward decolonized, community-led engineering research that supports Indigenous self-determination and aligns with the UN Declaration on the Rights of Indigenous Peoples. Advancing equity in infrastructure requires engineering practices grounded in sustainability, cultural relevance, and meaningful reconciliation
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.
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 | 0.022 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.029 | 0.046 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".