Listening to the Quiet Voices: Unlocking the Heart of Engineering Grand Challenges
Bibliographic record
Abstract
According to the National Academy of Engineering, the list for the Grand Challenges for Engineering are: (1) Make solar energy economical; (2) Provide energy from fusion; (3) Develop carbon sequestration methods; (4) Manage the nitrogen cycle; (5) Provide access to clean water; (6) Restore and improve urban infrastructure; (7) Advance health informatics; (8) Engineer better medicines; (9) Reverse-engineer the brain; (10) Prevent nuclear terror; (11) Secure cyberspace; (12) Enhance virtual reality; (13) Advance personalized learning; and (14) Engineer the tools of scientific discovery. Surely, it may be difficult to find many who would find any reason to disagree with the identification of any of these topics for both the present and future engineers. Rather than object to what is included, I would like to raise the issue of what has been neglected in this list and far too often in engineering—listening to the quiet voices that speak from within each of us from our heart. I am suggesting the act of listening as one additional entry for this most important list.In my view, one set of skills that our profession does not encourage very well is stopping and listening—stopping and listening to each other, stopping and listening to life around us, or stopping and listening even to ourselves. This is a skill that, given the pace of our modern society, technological advances and our cultural conditioning, must be cultivated for it likely will simply either never develop or quickly wither away. The question at hand then becomes how does one cultivate the ability to stop and to listen? The present work offers one such path though clearly there are countless others.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.031 |
| Scholarly communication | 0.025 | 0.037 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.014 | 0.031 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".