Competing Values in the Management of Innovative Projects: The Case of the RandstadRail Project
Bibliographic record
Abstract
Large engineering projects without late delivery, cost overruns or technical problems seem to be rare (Flyvbjerg et al., 2003). Illustrations of this statement are abundant worldwide (e.g. the French Superphenix project, the German Transrapid project, the Channel Tunnel, Denver International Airport, Boston’s Central Artery Tunnel (Dempsey et al., 1997; Bell, 1998; Altshuler and Luberoff, 2003; Flyvbjerg et al., 2003). The political and societal environments of these projects all ask for safe delivery on time and within a budget. A variety of project management tools have been developed to meet such expectations. However, these projects also have innovative elements, providing situations that implementers (e.g. managers, engineers, operators) of the projects have not met before. These elements require room for improvisation and interaction between implementers, which most project management tools typically do not provide.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".