Bibliographic record
Abstract
Standard engineering education often focuses on disseminating specialized, technical knowledge with the overall goal of training competent designers and decision-makers.Students learn to reach a desired outcome by focusing on improving the efficiency of the object or procedure in question; however, the social, historical and environmental context in which this problem exists is often dealt with marginally or completely ignored.As a result, in engineering practice, unexpected undesired outcomes often arise out of actions that were intended to improve a particular problem.As a student, I have experienced two different engineering programs, each with a unique approach to addressing the lack of context in engineering education and practice.During my undergrad, I took part in the Engineering and Society program at McMaster University, and during my current graduate work, I am a part of the Centre for Technology and Social Development at the University of Toronto.Each program attempts to teach students how to think more broadly, balancing breadth and depth in order to develop a new approach to engineering problems.The Engineering and Society program uses a technique called "inquiry" throughout the curriculum and encourages engineering students to focus on a discipline outside of engineering throughout their undergraduate education.The Centre of Technology and Social Development builds a historical context for understanding the interaction between technology, society and the biosphere through a series of courses.Each program has benefits and drawbacks.This paper will discuss my personal experience in these programs and discuss a way in which the advantages of each one could be combined in order to help improve the overall engineering education experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".