Co-design of water services and infrastructure for Indigenous Canada: A scoping review
Bibliographic record
Abstract
There is movement in engineering fields and in Indigenous communities for enhancement of local participation in the design of community infrastructure. Inclusion of community priorities and unique cultural, spiritual, and traditional values harmonize the appearance, location, and functionality of developments with the social and cultural context in which they are built and contribute to holistic wellness. However, co-design processes that align community values and the technical needs of water facilities are difficult to find. A scoping review was conducted to explore the state of knowledge on co-design of water infrastructure in Indigenous Canada to build a knowledge base from which practices and processes could emerge. The scoping results revealed that articles and reports emerged only in recent years, contained case studies and meta-reviews with primary (qualitative) data, and involved community members in various capacities. Overall, 13 articles were reviewed that contributed to understanding co-design for water infrastructure in Indigenous Canada. Barriers to co-design included funding models for Indigenous community infrastructure, difficulties in engineers and designers understanding Indigenous worldviews and paradigms, and a lack of cooperation among stakeholders that contribute to ongoing design failures. A working definition of co-design for Indigenous water infrastructure is presented.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".