Manageability of Complex Construction Engineering Projects: Dealing with Uncertainty
Bibliographic record
Abstract
Copyright © 2009 by Martijn Leijten. Published and used by MIT ESD and CESUN with permission. Complex underground construction projects appear to suffer from high levels of unmanageability caused by the gap between the information required to build the systems and the information available to the decision-makers. Many project managers attempt to address this problem by increasing the information available to them, often using input from hired stakeholders. Two example projects, Boston’s Central Artery/Tunnel Project and The Hague’s Souterrain, show that this strategy can cause even more uncertainty than it solves. The information providers may provide the information with strategic values in mind and the decision-makers may misinterpret the information due to an overemphasis on objectifiable, quantifiable information and criteria, while disregarding less tangible potential causes of deviance. The solution may be found by reconsidering the incentives in the project organization.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".