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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".