Public Stewards or Hired Guns?: An Inquiry into Engineering Education
Bibliographic record
Abstract
The world is facing converging crises of overpopulation and urbanization, resource depletion and globalwarming, political tension and unrest. Often, we call upon engineers, as technical experts, to addressthese issues. Engineers are allotted a large amount of decision‐making influence and power based ontheir assumed knowledge and skill sets. However, there is perhaps a danger in giving influence toindividuals with a limited understanding of social, political and environmental issues. In the words of Donna Riley, “The profession of engineering…has historically served the status quo, feeding an ever‐expanding materialistic and militaristic culture, remaining relatively unresponsive to public concerns, and without significant pressure for change from within” (Riley, 2008). This inquiry seeks to ask: “Are engineers truly prepared to tackle today’s contemporary issues?” by exploring the nature of the engineering industry in the 21st century, engineering education broadly, and the Applied Science Curriculum at Queen’s University specifically. It will draw on both independent research and focus groups with Applied Science students. Particular themes which will be explored include the engineering design process, the understanding of power relationships and the need for interdisciplinarity.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.024 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| 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".