A Rising Tide Lifts All Boats, Ignoring Risks Can Sink Them: The Peril of Rework in Large-Scale Transport Projects
Bibliographic record
Abstract
Rework can be a significant and costly problem during the construction of large-scale transport (>$500 million) projects. There is, however, limited understanding and knowledge about the underlying dynamics and causal mechanisms of rework. Both the public and private sector organisations tend to ignore the costs of rework and thus have been unable to contain and manage its risks. This article takes a look at the wicked and inter-organizational problem of rework and invites the public and private sectors to work in unison to thwart this risk. The paper makes a twofold contribution: (1) it calls on the public and private sectors to consider the likelihood of rework as a part of their risk management strategy; and (2) suggests that there is need use a smart data approach to ‘anticipate what might go’ wrong in terms of rework so as to deliver large-scale transport projects successfully.
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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 | 0.039 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".