Use of Aircraft Crash Cases in Teaching Engineering (with notation)
Bibliographic record
Abstract
Discussions of engineering disasters have been widely used in teaching engineering ethics.However consideration of such disasters can also be used in a number of other ways in engineering education.For example, engineering disasters can be used to discuss operational aspects of engineering which are often not considered in the teaching of engineering, and they can be used to illustrate how operational problems, if properly analyzed, can be used in improving engineering devices and processes.The discussion of engineering disasters can also be used to illustrate the importance of using correct and adequately monitored maintenance procedures in the operation of engineering devices and systems.The discussion of engineering disasters can also be used to illustrate many aspects of engineering science.As well, such discussions can also be used to illustrate how difficult it is in many cases to determine the cause of a failure.Commercial aircraft crashes provide a rich source of material for use in such teaching and some examples of such crashes and of how they can be used in engineering education are discussed in this paper.[This paper was found by ASEE and other publishers to contain a significant amount of duplicate material from a paper delivered by the author at the 3rd International CDIO Conference, MIT, Cambridge, Massachusetts, USA, June 11-14, 2007 and from other sources not related to the author.Responding to ASEE's findings of duplicate material from the previous presentation by the author, the author stated that both the ASEE and CDIO papers were part of a series of studies centered around a single topic area and that some duplication between papers describing various aspects of the work is almost inevitable.]
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".