A HOLISTIC, INTERDISCIPLNARY APPROACH TO THE DESIGN OF A SUSTAINABLE PERSONAL MOBIILTY SYSTEM
Bibliographic record
Abstract
-For the past several years, undergraduate engineering students at Western University have been working on the design of a sustainable personal mobility system (SPMS). The vision of SPMS is to replace privately owned conventional cars with a sustainable mobility system built around shared lightweight electric vehicles, powered by renewable energy. In the 2011-2012 academic year, 13 undergraduate engineering students worked in five teams to further develop SPMS. They examined the economic and business feasibility of SPMS, and developed conceptual designs for the car-sharing system and vehicles. They reviewed the issues around climate change and renewable energy, and added to previous work by exploring the literature from different disciplines to learn more about car-sharing systems, electric vehicles, traffic network modeling and simulation, Monte Carlo simulation, vehicle layout and ergonomics. The students learned to take a holistic, interdisciplinary approach to the complex sociotechnical problem of sustainable personal mobility. While many students were initially uncomfortable with the ambiguity and ambitious scope of the project, they came to appreciate the many sociotechnical factors involved in designing an SPMS. An important learning outcome has been to increase student awareness and understanding of the environmental challenges facing society, as well as possible sociotechnicalsolutions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".