Test Environments to Analyse Methodological Improvements of Cost-benefit Analysis for Transport Interventions
Why this work is in the frame
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Bibliographic record
Abstract
Planners and policymakers are concerned that cost-benefit analysis (CBA) rankings are so sensitive that even minor adjustments in contentious input parameters might result in drastically different policy recommendations. Although there is a need for methodological improvement, CBA seems to retain its role as the most coherent and robust framework available for project appraisal. Based on the mentioned need for improvement, this paper aims to create a test environment to analyse possible methodological advances in transport CBAs. This test environment consists of three different models based on typical transport interventions. The models have different levels of complexity and computational need. The sensitivity of each model was tested, and the most critical factors were identified. The majority of the economic benefits come from travel time savings, so the value of time was identified as the five most sensitive factors for all cases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it