Bibliographic record
Abstract
Because the United States has taken steps in recent years to invest significant funds to implement high-speed rail, it is important to consider how high-speed rail has developed elsewhere since its original deployment between Tokyo and Osaka, Japan, in 1964. The three models of highspeed rail development that have emerged over the past 50 years are (a) the exclusive-corridor strategy exemplified by Japan, (b) the hybrid-corridor strategy found in France and Germany, and (c) the comprehensive national network strategy pioneered by China. The relevance of these three models to high-speed rail development in the United States is conditioned by several factors, including previous attempts to develop high-speed rail in the country, the institutional organization of the existing U.S. freight rail industry, and external market dynamics. Although the U.S. railroad industry has evolved differently and moved apart from rail operations in many other countries, external market dynamics such as growing urban populations and increased demand for passenger rail transportation appear to be converging with those in the rest of the world. Accordingly, several lessons are drawn for the United States from global high-speed rail experience: (a) a higher degree of relevance and higher ease in knowledge transfer would occur from following the exclusive-corridor model, (b) less difficulty in knowledge transfer but less relevance would arise in adopting the hybrid-corridor model, and (c) more relevance but more difficulty of knowledge transfer accompanies pursuit of the comprehensive national network model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.126 | 0.047 |
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".