Creative destruction: building toward sustainability
Bibliographic record
Abstract
The engineering community at large, and the civil engineering community in particular, has the opportunity and arguably the obligation to promote a development agenda that considers not only the economics of development, but also the health of the environment and society at large. In this paper, we contemplate the challenge of sustainable development and its effect on project scale and scope. We discuss the inherent opportunity to drive the "creative destruction" of the development industry, using innovation to exploit inefficiencies in the planning and management of engineering systems to create a range of "future" products and services that challenge existing practice. We review the impact of procurement policy, contract pricing, prescriptive codes, and public policy on innovation. Several examples of innovative design and sustainable development introduced into the planning and management of Canadian civil engineering projects are provided. We assert that the most effective means of promoting the sustainability of built environment and civil infrastructure systems will be through inter- and intra-industry collaboration with the support of public policy-makers.Key words: sustainable development, civil, engineering, infrastructure, innovation, creative destruction, environment, collaboration.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.049 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".