Rethinking the "Impact of Engineering on Society and the Environment"
Bibliographic record
Abstract
The current CEAB attributes, including “the impact of engineering on society and the environment,” represent a welcome shift in engineering education towards acknowledging the sociotechnical nature of engineering practice. The “impact” attribute in particular also demonstrates a need and an opportunity for engineering educators and scholars with interdisciplinary backgrounds to meaningfully contribute to engineering education. In this paper, we offer reflections on and constructive criticism of the “impact” graduate attribute, drawing from our personal experiences as learners and teachers, and from literature in Science and Technology Studies, Engineering Studies, and the Learning Sciences. We develop three related critiques of the current graduate attribute, focusing on its presentation of relationships, directionality, and power. While the inclusion of the “impact” attribute represents a significant step for Canadian engineering towards a more just, sustainable, and decolonial vision for the future of the profession, we argue that the current attribute also reinforces dominant engineering ways of thinking about engineering-society relationships. We conclude the paper by offering a reworked version of the graduate attribute as a provocation and a starting point for discussion.
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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.114 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".