UNDERSTANDING THE INTERPRETATON, IMPLEMENTATION, AND IMPACT OF ENGINEERING ETHICS EDUCATION IN CANADA
Bibliographic record
Abstract
Engineers have direct influence on the evolving planet. With the fundamental goal of continually creating a better world, it is essential for engineers to meaningfully understand ethical responsibility and the impact of engineering on society and the environment [4, 15]. Although efforts have been made to identify the objectives of engineering ethics education (EEE), little has been done to thoroughly investigate the impact EEE is having on individuals’ ethical development [6, 7, 13]. Furthermore, there is a large level of uncertainty as to the amount of exposure students have to EEE between programs, as well as the variability of ethics content students experience as a result of diverse interpretation of EEE objectives. The amount of exposure and type of content students are exposed to will affect the impact EEE has on them and hence, it is important to evaluate these aspects of the current implementation of EEE in Canada. This paper will review literature regarding the current state of EEE within Canada and the objectives of EEE, as well as propose a study to investigate students’ experience with EEE throughout undergrad and the impact that engineering ethics education may have on their ethical behaviours within an engineering context.
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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".