Canadian Engineering Ethics Game
Bibliographic record
Abstract
This thesis covers the design, implementation, and review of a video game designed to assist Canadian Engineering Interns in understanding and contextualizing engineering ethics. This understanding is essential during their professional practice exam and subsequently in their day-to-day lives as engineers. In engineering schools, engineering ethics is traditionally taught either as a philosophical examination of how engineers should act or as rote learning of the act, by-laws, and code of ethics that govern engineering practice. Most importantly, in the context of undergraduate engineering education, the amount of coverage is limited, and students are all too often focused on what is needed for the test, not mastery of the material for their own understanding. Unlike university courses, playing this game is voluntary, no grades are assigned, and players are expected to game the system by choosing poor responses just to see what will happen to them. Learning occurs through exploring cause and effect relationships, by making ethical choices and experiencing how decisions often have trade-offs or conflicting right answers. To encourage reflection, players were asked to think about the cases, and how they reacted to the unprofessional behaviour of characters in the game, through this reflection process, players are encouraged to grow, understand, and adopt professional behaviours. The research methodology was to create a proof of concept video game featuring five case studies of conflicts that an Engineer or Engineering Intern might reasonably encounter in their professional practice. The game then went through a design review, in which sixteen Professional Engineers and Engineering Interns played the game and reviewed the cases in detail to provide feedback on their realism and identify areas for improvement. Based on the feedback from testers, the concept is sound, addresses a need within the engineering community and merits further research.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.008 |
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".