The Trade-Offs of Transferring Demand Risk on Urban Transit Public–Private Partnerships
Why this work is in the frame
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Bibliographic record
Abstract
There is a long history of ridership on urban rapid transit projects failing to meet predevelopment forecasts. This article examines the trade-offs for government associated with transferring the financial risk of ridership demand shortfalls to the private sector through public–private partnerships (PPPs). First, the article develops a theory of the way that PPPs are designed to clamp down on the causes of transit ridership shortfalls. Second, it outlines technical, planning, and financial trade-offs associated with transferring ridership demand risk to the private sector. Third, examples are presented to show how these trade-offs manifest in the most popular models of allocating ridership demand risk in PPPs. The article concludes that transit projects have particular characteristics that challenge the effective transferring of ridership demand risk to the private sector. Governments should instead focus on project procurement models that encourage risk sharing between the partners.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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