ETHICS 2.0: AN INTROSPECTIVE APPROACH TO UNDERSTANDING AND TAKING OWNERSHIP OF YOUR ACTIONS
Bibliographic record
Abstract
ECE290 (Engineering Profession, Law andEthics) is a second year core course in the undergraduateElectrical and Computer Engineering program at theUniversity of Waterloo. This course was designed to moveaway from achieving desired ethical outcomes or “right”answers and instead to focus on refining individualdecision-making processes. Ethics was framed asstemming from the fundamental identity question faced byeach individual and the core beliefs held by the individualwith an aim to make the course more personal, engagingand introspective for the student. Students also gainedpractical experience in deconstructing their own identityby identifying and understanding master behavioralpatterns and the perspectives of various characters inpertinent literature and case studies where ethicalambiguity is at the forefront. Evaluation and testingmethods for the ethics component of the course weredesigned to focus on evaluating the depth and breadth ofthe students’ decision-making processes. Student courseevaluation questionnaires had a response rate of between60-75% and indicated that students strongly believed theywere being encouraged to think critically and reasonindependently
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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 | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".