Bibliographic record
Abstract
We present a cap-and-trade scheme for the regulation of ridesharing. As opposed to marginal-pricing schemes, cap-and-trade schemes limit the quantity of transportation. Recognizing that a central authority may not be able to adequately regulate quantity, we let the quantity be determined according to demand for ridesharing. We use demand to compute the social cost of selfish driving in a virtual world where ridesharing does not exist and set this cost as a limit on the amount of social cost that a transportation network company (TNC) can incur. We perform analysis in the static case to show that our scheme has the effect of incentivizing the positive effects of ridesharing, i.e., carpooling, while limiting its negative effects, e.g., deadheading. We also present and discuss a practical implementation of the scheme. In implementation, the virtual social costs would be issued as credits through a central service and the actual social costs would be issued as debits; a net-positive balance would be imposed by the central service and TNCs could trade credits and debits on the open market.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".