Work in Progress: A Missing Narrative of Violence in Engineering Education
Bibliographic record
Abstract
Violence is prevalent in many areas of engineering practice. Engineers engage in and support projects that are demonstrably violent against living beings and systems, including future generations of humans. Generally neglected in engineering education in Canada is a nuanced narrative of how violence broadly influences engineering decision making, especially when viewed through topics such as decolonization, equity, diversity, and inclusion (EDI). This work examines types of violence prevalent in engineering with a particular emphasis on the role of both violence and counter-violence in engineering practice. EDI are reframed using a new inclusive foundation of moral consideration, showing a natural connection, though violence, between principles of decolonization and sustainability. A new, semi-quantitative framework is proposed for incorporating questions of violence into project decision making intended to prompt discussions about the impact and utility of violence in engineering projects.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.067 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".