Great Problems of Grand Challenges: Problematizing Engineering’s Understandings of its Role in Society
Bibliographic record
Abstract
The U.S. National Academy of Engineering’s Grand Challenges for Engineering report has received a great deal of attention from legislators, policymakers, and educators, but what does it entail for social justice considerations in engineering? This article situates the Grand Challenges report as a cultural artifact of the engineering profession—an artifact that works to reinforce engineering’s professional culture, recruit new members, and reassert engineering’s legitimacy in the 21st century. As such, the Grand Challenges report provides a unique opportunity to understand and critique the role engineering envisions for itself in society. The articles in this special issue of IJESJP identify four central critiques of Grand Challenges: authorial particularism, double standards in engineering’s contributions to these challenges, bracketing of the “social” from “technical” realms, and deterministic definitions of progress. These critiques call for increased reflexivity and broadened participation in how engineers define problems and attempt to solve them.
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 | 0.050 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.022 | 0.124 |
| Scholarly communication | 0.030 | 0.038 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.011 | 0.021 |
| 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".