Bibliographic record
Abstract
Abstract In a climate of global disruptions, it seems certain that adaptive change will be an important part of our lives for the foreseeable future. Now, more than ever, we have a responsibility to introduce our students to skills that will support them in this work. This paper is a synthesis of a number of invited plenary talks given from 2014–2019 which explore the dichotomy of engineering: we are innovators and problem solvers who also hold reliability and protection of the public as our most important obligation. The data show that innovation will become more important in the decades ahead, but finding academics who love the uncertainty that comes with teaching design is becoming more difficult. The tension—and occasional polarization—between prioritizing innovation or maintaining reliable known solutions is deconstructed and explored. Two examples of how this plays out in industry are presented. The second half of the paper presents a selection of important ideas for teaching innovation thinking. Practical teaching strategies are included for easy implementation in the classroom. In closing, the major threads in (engineering) teaching and learning research are summarized, again, with samples of best practices in the context of teaching collaborative innovation. The goal of this paper is to provide engineering instructors with tools which are easily applied to build curiosity and an innovation mindset, without reducing their commitment to reliable and robust engineering problem solving.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".