اخلاق در حرفه مهندسی
Bibliographic record
Abstract
Human achievements have led to large advancements in technology which along with the beneficial and valuable effects have also had catastrophic social consequences due to the lack of proper use and management. Therefore, ethics are considered as one of the critical components in the growth of science and culture. Engineers have a high social status with privileges that come with it. In return, it is expected from them to be responsible in their profession and carry out their duties as well as satisfying expectations of the public, protection of the environment, preventing the occurrence of catastrophes, and acting as role models for the next generation which incorporates both macro and micro dimensions of ethics. Training ethics in different educational levels (primary, secondary, and high school) facilitates the compliance at university level, which then increases the students’ flexibility in learning ethical problems in the engineering profession. The participation of students and engineers in compiling ethical instructions increases their effectiveness, and enables each generation to solve their social and professional problems. In this paper, some case studies such as the collapse of the Quebec bridge, the Chernobyl disaster, the Fukushima catastrophe, the gradual drying of Lake Urmia, groundwater table drop, land subsidence, and unsustainable development of modern irrigation are investigated. The engineering mistakes, the decisions taken, and how the crisis was managed based on the ethical aspects of each of the aforementioned test cases are investigated for their carelessness, negligence, ignorance, and deliberate mistakes.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.272 | 0.265 |
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".