Bibliographic record
Abstract
There is a growing trend in engineering education to increase the societal awareness among theengineering graduates, so that the engineering solutions proposed by the engineers are more sustainable. To achieve this, one of the efforts in Concordia University is to ask capstone students to discuss and implement (wherever possible) ethical, legal, social, environmental, and entrepreneurial aspects of their capstone design. Students are given two lectures during the capstone year which provides them with prompts to identify and think beyond their personal biases and perceptions of the society. At the end of the term, each capstone team is asked to define engineering failure. The aim for this is for graduating students to have a well thought of idea of the engineering design failure before they enter the workplace. This article explains the two phases (lectures) of the capstone lectures related to the ethical, legal, societal, environmental, and entrepreneurial aspects of an engineering design. Additionally, the article aims to analyze the definitions of engineering failure submitted by the engineering students at the end of the capstone year to identify keywords and terms that the graduating engineering students attribute to success and failure of an engineering design. The objective of the paper is to open the discussion among engineering educators for incorporating ideas in their courses that can improve engineering students’ understanding of a sustainable design and assess the success of these strategies.
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 | 0.028 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".