Engineering Improvement: Social and Historical Perspectives on the NAE’s “Grand Challenges”
Bibliographic record
Abstract
The list of engineering "Grand Challenges" lately developed by the National Academy of Engineering enters a long historical tradition of such epically scaled to-do lists, dating back to the profession's U.S. origins in the mid-19th century. The mission statements, codes of ethics, and, later, lists of so-called grand challenges that have issued from engineering societies have served the dual function of directing engineers' work and supporting particular cultural roles for these bodies of experts. Almost all such plans, regardless of period or sponsoring body, have also blended highly practical aims of industrial and infrastructural development with more inchoate projects of societal uplift. The Grand Challenges of the NAE, currently playing a formative role in many engineering organizations and research and teaching settings, extend this lineage, working from a selective and self-confirming view of human welfare. We might bring to the Grand Challenges the type of critical, politically informed analysis that historians and STS scholars have brought to other sites of engineering activity and professionalization, to detect the nature of interests that underlay all such projections of engineering’s role in society. Who is served by the development of different technologies, products, and infrastructures? Who might be harmed? Most fundamentally, the Grand Challenges proceed from the premise that engineering research, construction, invention, and production are to take precedence over their absence, as befits a body dedicated not to the contraction of such enterprises but to their extension. Yet the interests of sustainability, global health, and other areas of human well-being might be best served in certain cases by just such a turning away from engineering. Making explicit the social and historical assumptions of the NAE’s Grand Challenges, and probing the implications of those assumptions for a diverse range of actors and communities, may pave the way for more thoughtful engagement with the humanistic and democratic potential of engineering.
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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 | 0.025 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.028 | 0.107 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".