Large Program Cost Estimating: Nuanced Implementation Assumptions Make Non-Nuanced Impacts
Bibliographic record
Abstract
Transformative transportation programs often comprise discrete complex projects to deliver an overall vision. Even with substantial benefits, the cost, complexity, and risk of the entire program often creates a “sticker-shock” reaction that dominates public discussion. When escalation is factored into the program, costs can appear unreasonable or not financeable. In this scenario, benefits and costs are never discussed in a parallel (and equal) basis. If this leads to more delay, escalation creates an ever-more expensive output, leading to ever less support for the transformative program. Aviation and highway industries often deliver their programs as discrete projects of independent utility (for example, an additional lane of highway, or a new airport terminal). Rail and bus transit infrastructure projects, in contrast, are usually presented as the entire program (new vehicles, infrastructure, stations, facilities) even if some aspects of the program could be presented as a discrete project. By focusing on the program, rather than projects, transit proposals present stretch delivery dates and feature mountains of escalated costs. This paper uses the recent Boston North-South Rail Link (NSRL) Reassessment (Massachusetts, U.S.) as a case study. An examination is made of the potential benefits of packaging the program to advance independent but complementary projects. The expectation is that this approach reduces escalation, resulting in overall program cost savings in real dollars. While further research to validate this premise is suggested, the concepts of the paper can be used to reassess traditional megaproject schedule and delivery assumptions, reducing the identified cost escalation over the years.
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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 | 0.027 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".