Evaluating urban railway development projects: an international comparison
Bibliographic record
Abstract
This paper compares cost benefit analysis (CBA) approaches to urban rail project evaluation in Australia, the US, the UK, Canada, New Zealand, Germany, Holland, France, Japan, Hong Kong, the Republic of Korea and Singapore. The key findings on the different aspects of the CBA framework from a strategic viewpoint, as well as the different parameter values adopted, are presented. In each case the analysis presented is based on published evidence. Published evidence can lag behind the practice of evaluation in this field and hence this exercise may not have included the latest development in national CBA applications. This is a limitation which this research has had to accept. In most cases published national guidelines were used to inform about CBA approaches. For Japan, France, Germany, Hong Kong, Republic of Korea and Singapore the guidelines are not published or available for our analysis. In these cases, CBA approaches were derived from research papers or obtained via email correspondence with the relevant authorities. (a) For the covering entry of this conference, please see ITRD abstract no. E217541.
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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 | 0.039 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".