What can engineers learn from the past? A potential role for history in engineering education.
Bibliographic record
Abstract
At present, in many societies, engineers play a significant role in solving problems of energy, transport, accommodation and production; but similar problems have been solved through technical and non-technical means for thousands of years. Numerous historical examples therefore exist, in which the ends of different approaches to problem-solving are apparent: some tending to produce socially and/or ecologically sustainable outcomes, and some less positive. Historians do not simply narrate the past, they explain and interpret changes and continuities by paying attention to larger issues of, for example, class, gender, polity and economy. Such historical narratives, we argue, may have a useful role to play in efforts to shift the perspective of engineering students away from a narrow focus on complex technical solutions, towards the broader context in which their problem-solving will take place. This ability to assess the relationships between engineering problem-solving and the broader social and environmental context is critical to the development of a more sustainable and socially-just engineering practice.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.015 | 0.032 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".