A Practical Application of Code of Ethics in Failure Case Studies
Bibliographic record
Abstract
Several packages of failure case studies have been published by the Education Committee of the ASCE Forensic Engineering Division aiming to promote failure literacy of professionals and students in the architecture, engineering, and construction industries. This paper discusses the efforts of the committee to compile a collection of failure case studies of ethics, including design flaws of Citicorp Building (NY, US), Harbour Cay Condominium collapse (FL, US), Rana Plaza collapse (Savar Upazila, Bangladesh), Sampoong Superstore collapse (Seoul, South Korea), Versailles Wedding Hall (Israel), Hyatt Regency Walkway collapse (MO, US), Montreal Olympic project management failure (Montreal, Canada), and Flint water crisis (MI, US). The paper discusses the overview of three cases in ethics and provides guidance on using these cases to understand and interpret the code of ethics of professional engineers. Lessons learned from these cases are presented, reaffirming that all professional engineers should commit to ethical responsibilities in society, the natural and built environment, the profession with regards to their clients, employers, and peers. It concludes that the health, safety, and welfare of the public should take precedence over all other responsibilities of practicing professional engineers.
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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 | 0.237 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.015 | 0.031 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".