On Two Wheels in Paris: The Vélib' Bicycle-Sharing Program
Bibliographic record
Abstract
French advertising company JCDecaux and the city of Paris jointly developed Velib, a wildly popular bicycle sharing system. Despite Velib's public appeal, vandalism and theft led to ballooning operating costs-costs borne by JCDecaux alone. The two parties opted to renegotiate their contract, which would impact prices, revenue sharing, cost allocation, and the operation of the system as a whole. Could the parties agree on a common strategy that would meet their objectives, while still delivering a first class bicycle sharing service to the city of Paris?Learning Objective: The case serves two primary pedagogical purposes. First, the negotiations and contractual agreements between the city of Paris and JCDecaux provide insight into issues both sides face in navigating public-private partnerships. The tasks at stake include designing contracts to align diverse objectives, handling lock-in, coming to agreement on pricing, and anticipating renegotiation strategy. Second, the case considers the unique operational challenges of providing a shared resource and explores how technological and market-based innovations can help overcome these challenges.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.002 |
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".