Bibliographic record
Abstract
Introduction 1132 I. Analyzing Transit and Jobs for Sustainability 1135 A. The Importance to Society and Sustainability of Transit and its Proximity to Jobs 1135 B. Methodologies for Transit and Job Relationships 1136 C. General Patterns and Trends Nationwide for Selected Bus and Rail Transit Systems 1137 II. Heavy Rail and Other Fixed Guideway Modes 1138 A. Shares of Transit Modes Relative to Other Modes 1138 B. U.S. Rates of Change in Heavy and Light Rail Transit 1140 III. HR Systems and Job Characteristics Around HR Stations in New York City 1141 A. Job Density Change 1142 B. Job Density or Concentration 1144 IV. Connectivity and Support Among Transit Systems 1146 V. Factors Influencing the TOD/Job Phenomenon 1147 Conclusion 1151 INTRODUCTION Transit has supported sustainability within, around, and connected to urban areas through job attraction, environmental and climate benefits, and other job-related benefits associated with proximity to transit. The extent to which jobs are attracted to transit varies by urban area, type of rail system, and employment sector. (1) The relationship between jobs and transit accessibility is an important component of sustainability. Development around transit is typically referred to as transit-oriented development (TOD). The U.S. Environmental Protection Agency (EPA) has defined the concept as a compact development built around a transit station or within easy walking distance (typically a half-mile) of a station and containing a mix of land uses such as housing, offices, shops, restaurants, and entertainment. (2) The type of development varies. The concept of the TOD is an old one, and a review by Ian Carlton linked it directly to sustainability and identified its introduction initially with Peter Calthorpe and the expansion of the term by others. (3) Other reviews similarly support the relationship between rail transit and job density. (4) Transit systems are typically categorized as including light rail transit (LRT), bus rapid transit (BRT), street car transit (SCT), commuter rail (CR), and heavy rail (HR). (5) In his article, Professor Nelson evaluates LRT, SCT, and BRT with respect to job attraction, transit accessibility, and characteristics of urban areas that support the job and transit relationship for groupings of certain economic sectors within defined alternative radii around transit. (6) Studies other than Nelson's have examined this relationship using fewer nodes and geographic areas. (7) Nelson's study included eleven LRT, three SCT, and eight BRT systems. (8) These account for about half of the LRT systems, about a third of the SCT systems, and all of the BRT systems in the U.S. (9) The LRT, SCT, and BRT transit systems serve more decentralized populations relative to those served by HR; LRT, SCT, and BRT have been important drivers of TODs, as Nelson observes, and their growth rates support Nelson's focus on these systems. (10) LRT in particular has grown substantially in terms of the number of systems, passenger trips, and miles traveled, as indicated by the analyses of data from the American Public Transportation Association (APTA) and the U.S. DOT National Transit Database (NTD). (11) While LRT, SCT, and BRT modes generally account for a lower share of transit ridership, they have been shown to have grown the fastest during many time periods according to the APTA and NTD. (12) This Article complements Nelson's emphasis on LRT, SCT, and BRT in highlighting how HR transit also supports job growth. Given the higher ridership that HR can and does support, it has the potential to attract jobs on a larger scale. …
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".