Perceptions of Engineers' Environmental Responsibility and Professional Codes of Ethics
Bibliographic record
Abstract
In Canada, provincial and territorial level Professional Engineering Codes of Ethics (PECoEs) derived from national guidelines presented by Engineers Canada, provide principles for engineers to aid in decision making and to evaluate the ethical correctness of professional behaviour [1]. Engineers Canada states that engineers must, “Hold paramount the safety, health and welfare of the public and the protection of the environment and promote health and safety within the workplace” [1]. This is frequently the only guideline directly related to the environment included in Canadian provincial and territorial PECoEs)[2-13]; Notably, Ontario’s PECoE currently does not explicitly mention the environment in any capacity [14]. Present professional engineering ethics guidelines for environmental responsibility are either missing or largely open to interpretation in Canada, and complex environmental issues may require more robust ethical frameworks to be effectively approached long-term within engineering industry. Developing PECoEs requires a better understanding of how engineers view their ethical responsibility with respect to the environment. This paper outlines a study to investigate the ethical beliefs and PECoE interpretations of participants through an online survey with ethical case studies, and interviews. The ultimate goal of this research is to aid the development of PECoEs and engineering ethics education to support sustainable practice.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".