Lock-in and its Influence on the Project Performance of Large-Scale Transportation Infrastructure Projects: Investigating the Way in Which Lock-in Can Emerge and Affect Cost Overruns
Bibliographic record
Abstract
Lock-in, the escalating commitment of decision makers to an ineffective course of action, has the potential to explain the large cost overruns in large-scale transportation infrastructure projects. Lock-in can occur both at the decision-making level (before the decision to build) and at the project level (after the decision to build) and can influence the extent of overruns in two ways. The first involves the ‘methodology’ of calculating cost overruns according to the ‘formal decision to build’. Due to lock-in, however, the ‘real decision to build’ is made much earlier in the decision-making process and the costs estimated at that stage are often much lower than those that are estimated at a later stage in the decision-making process, thus increasing cost overruns. The second way that lock-in can affect cost overruns is through ‘practice’. Although decisions about the project (design and implementation) need to be made, lock-in can lead to inefficient decisions that involve higher costs. Sunk costs (in terms of both time and money), the need for justification, escalating commitment, and inflexibility and the closure of alternatives are indicators of lock-in. Two case studies, of the Betuweroute and the High Speed Link-South projects in the Netherlands, demonstrate the presence of lock-in and its influence on the extent of cost overruns at both the decision-making and project levels. This suggests that recognition of lock-in as an explanation for cost overruns contributes significantly to the understanding of the inadequate planning process of projects and allows development of more appropriate means.
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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 | 0.013 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".