Special issue ‐ public transportation (I) — Recent methodological advances
Bibliographic record
Abstract
Public transportation is generally considered as an economically, financially, environmentally and socially sustainable form of urban transportation in major cities (World Bank, 1996).Promoting public transportation has long been one of the top priorities for both developed (European Conference of Ministers of Transport, 1973; Pucher, 1995) and developing (Armstrong-Wright, 1993) counties, avd is supported through a series of government interventions, such as public regulations, ownership restraints, operational priorities, direct or indirect subsidies, and privatization.The performance of public transportation is usually evaluated in terms of a number of important attributes such as cost, revenue, service ubiquity and reliability, patronage, and safety, ranging from a more localized perspective of "efficiency" to a wider perspective of "effectiveness" (Organisation for Economic Co-operation and Development, 1980; Fielding, 1992).An effective and efficient public transportation system benefits a society at large by reducing fuel consumption, preserving the environment, fostering development, reducing traffic congestion, improving safety, etc.However, ineffective and inefficient systems require excessive government subsidies, which impose a burden on taxpayers.Furthermore, in developing countries, poor levels of service on public transportation may lead to the vicious cycle and shift the transportation system to automobile-biased motorization at an early stage of development.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".