RATIONALE AND TEACHING OBJECTIVES FOR A CANADIAN ENGINEERING ETHICS GAME
Bibliographic record
Abstract
This paper uses Tyler’s rationale as a framework for analyzing the teaching objectives surrounding the design of a video game to teach Canadian engineering ethics.The two keys challenges in this area are defining what should be taught in engineering ethics and then how it is evaluated in order to demonstrate improved understanding. Traditionally, engineering ethics courses are taught as either codes of conduct, or based on case studies with very constrained courses. The evaluation that follows then uses the Defining Issues Test (DIT) or an instructor’s evaluation.However, the above methods could be improved by focusing on engineering ethics as a situated, embedded, and applied discipline. That is, one in which decisions are made as part of a team, embedded in a workplace whose goals will likely be in conflict with the engineers, and whose outcomes are unknown at the time decisions are made.By using a serious game in which the players are protagonists affords us the opportunity to present thick cases with multiple decision points and opportunities for players to demonstrate their ethical bias. Additionally, the progress of players and their interactions with non-playing characters can reveal information on their assumptions and ethical bias.
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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".