AN ADAPTED ENGINEERING DESIGN PROCESS: GUIDING TOUCHSTONES
Bibliographic record
Abstract
The field of engineering needs to develop while healing our relations with the lands, waters, and living systems. Fostering ethical spaces where Indigenous ways of knowing and being and western worldviews can hold space together, and cease to separate the technical from the social, are key to progressing equitably as a society. In the field of engineering within Turtle Island, it is essential that we adapt the engineering design process to reflect this. Following the execution of an Engineering and Architecture transdisciplinary Design Build course at University of Manitoba, and in partnership with the Shoal Lake No. 40 First Nation, it was acknowledged by stakeholders that further analysis of this project could establish lessons learned. This paper speaks to engineering education practice. The objective of this research is to develop recommendations for how the engineering design process can make space for Indigenous ways of knowing and being. Shoal Lake No. 40 community members, one engineering contractor, and four university faculty members were asked their perspectives on the development and implementation of two projects conducted with the community members and on the First Nation lands. Through the co-analysis of these open-ended discussions, recommendations were developed for how the engineering design process can integrate four touchstones external to the design process. The touchstones enable an engineer to perceive the design process and establish core intentions for a project that creates space for Indigenous values and principles and western worldviews.
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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 | 0.061 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".