A procurement policy-making pathway to future-proof large-scale transport infrastructure assets
Bibliographic record
Abstract
Governments worldwide have made a significant financial commitment to combat increasing traffic congestion and ageing transport networks over the next decade. However, large-scale transport projects are often late, over-budget, and below quality, making it difficult to future-proof assets and accommodate unanticipated changes. Evidence indicates that the traditional procurement model for large-scale projects used by Australian State Governments, for example, fails to deliver expected benefits. Markedly, a focused policy-making pathway is absent, especially for future-proofing these complex projects. Hence, the need to move away from a prevailing ‘understand, reduce, respond’ to a more adequate ‘understand, embrace, adapt’ attitude towards complexity and uncertainty in project procurement. The enabling functions of asset management, digitization, delivery, and finance might help. However, little is known about how they can coalesce to form a policy-making pathway to provide governments value for money outcomes and ensure assets are future-proofed. In this paper, we fill this void by reviewing the normative literature and proposing a conceptual approach. The issues we examine are of the utmost interest to governments worldwide as they grapple with designing, constructing, operating and maintaining transport assets that are both resilient to unexpected events and adaptable to changing needs, uses or capacities including climate change.
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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 | 0.054 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.010 | 0.011 |
| 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".