Ship to Shore: Integrating New York Harbor Ferries with Upland Communities
Bibliographic record
Abstract
Across the globe, from Vancouver to Istanbul to Sydney, ferries play an integral role in urban transit networks. In these world-class ferry systems, grand terminals function as neighborhood focal points, and ferry riders seamlessly transfer across transit modes using just one fare medium. Following decades of underutilization, New York Harbor is now in the midst of a ferry renaissance. New services whisk commuters and tourists across the Hudson and East Rivers, and, perhaps most significantly, the Mayor recently announced an unprecedented system expansion to restore ferry service to all five boroughs by 2018. However, in contrast to many of the world’s great ferry systems, ferries in New York tend to suffer from a marked disconnect from landside transit and community life, severely limiting their effectiveness as an extensive and equitable transportation system. Addressing this concern, this paper synthesizes data and qualitative information from interviews, case studies, academic papers, and government reports and proposes 15 actionable steps toward forging stronger connections—both physical and psychological in nature—between New York’s ferries and the upland communities they serve. Given the substantial investment the City has committed toward a five-borough ferry system, this research is timely and uniquely suited to influence ferry policy in New York City as it unfolds. Categorized by mass transit connectivity, fare integration, and bicycle and pedestrian access, these recommendations intend to guide City policy, increase ferry ridership, and optimize ferry service to help New York become one of the world’s great waterfront cities once again.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".