Developing the next generation of infrastructure engineers
Bibliographic record
Abstract
Infrastructure engineering is a complex multidisciplinary practice that underpins human health – the physical, mental and social, as well as economic well-being, of people. Infrastructure is unique because its defined value is related to the services and capabilities that it enables, not by any intrinsic value nor in the financial investments that make it possible. The complexities in infrastructure planning, as in other contexts, encourage simplification and standardisation of different types/systems during planning and design. Yet the authors argue that many of the assumptions that have led to the present are now less valid, and many of the challenges – whether pandemics, technological change or the evolving natural context – now create significant economic and societal risks. These influences tend to create a significant and growing demand for the most broadly informed infrastructure engineers, who not only can work in component system specialisations but can also thrive even within a larger system with all its complexities and interdependencies. The competent practice of infrastructure engineering is delivering the technical excellence of sector-specific infrastructure systems that are developed in sympathy with its dynamic operating context. The necessary professional competencies are not currently supported as they should be by a comprehensive and more complete educational foundation.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.018 |
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".