Rail ports: The collaboration of railroad and coastal steamboat companies in New England in the 1840s
Bibliographic record
Abstract
Prior to the development of railroad travel in New England, the primary mode of transportation was the stagecoach, a slow and uncomfortable vehicle. Steamboats traveled between coastal cities, but were more limited in their routes than stagecoaches and averaged the same amount of travel time. Desire for faster travel times, specifically between New York City and Boston, accelerated technological advancements that led to the development of a fledgling railroad network in New England by the second quarter of the nineteenth century. It also led to increased collaboration between railroad and steamboat companies, each offering combination routes to save time. The Long Island Railroad Company (LIRRC) was one of many railroad companies that took part in this transportation revolution. It was also one of many to experience financial hardship and one of the few to escape complete failure following the economic Panic of 1837. LIRRC trains departed Brooklyn and arrived in Greenport, at the northeastern end of Long Island, three hours later. Passengers then took a ferry across Long Island Sound to either Stonington or Norwich, Connecticut and then continued to Boston by railroad. This route was poised to cut travel time between New York and Boston nearly in half, but lasted only two and a half years. This study will examine the impact of financial hardship on the LIRRC’s quest to offer the fastest route between New York and Boston.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".