Bibliographic record
Abstract
The first study of the emerging curbside intercity bus industry, commonly called the Chinatown bus, is presented. The study of this relatively unknown industry addresses three research questions. First, why and how did the intercity curbside bus industry develop? Second, what services are operated by the curbside carriers and how do these services compare with competing travel options? Finally, how do the economics of operating curbside buses differ from those of traditional bus companies? The research speaks to policy questions about the appropriate role of regulation in transportation and the competition between private and public transportation providers. The findings indicate that in the past 10 years, curbside buses have grown to become an important transportation provider in the Northeast Corridor: more than 2,500 low-fare bus trips per week connect New York City to Washington, D.C.; Philadelphia, Pennsylvania; and Boston, Massachusetts. More than 100 buses depart each week to more than 30 other cities, traveling as far as Atlanta, Georgia, and Toronto, Canada. Traditional bus companies, such as Greyhound Lines and Peter Pan, have begun their own curbside intercity bus services either to mitigate the competition from new companies or because they recognize the competitive advantages of curbside operations. It is found that curbside bus operations offer significant cost savings compared with traditional bus services by lowering labor costs and avoiding terminal fees, although at the cost of limited accessibility for disabled passengers, reduced passenger safety, and other social concerns.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".