Foundational perspectives on ethics in engineering accreditation
Bibliographic record
Abstract
This chapter presents a historical and cross-national comparative examination of the formal incorporation of ethics and related learning outcomes in accreditation criteria for engineering graduates. The authors begin by exploring the origin of modern accreditation systems in higher education, emphasizing key developments in the United States over more than a century. They note more recent, widespread moves from inputs- to outputs-based frameworks, alternate quality assurance methods used in some non-US regions, and the continued global influence of US-style approaches to accreditation. They then present a series of specific cases to explore when, where, and how ethics and associated concerns have been formally codified in accreditation requirements for engineering graduates. They start with the United States as a well-documented and influential example and follow this with a description of two other Western/Anglo settings (the United Kingdom and Canada). They then turn to two international agreements (the Washington Accord and EUR-ACE) and two East Asian cases (Japan and China). Their account synthesizes prior scholarship and references some primary source materials, offering fresh new insight into the origins and development of engineering ethics education accreditation.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".