A Rising Tide Lifts All Boats, Ignoring Risks Can Sink Them: The Peril of Rework in Large-Scale Transport Projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it