Success by any other name would smell as sweet: different perspectives on success in public transport systems
Bibliographic record
Abstract
The issue of what it is that makes a public transport system successful is very important. Conventional indicators can be used to substantiate claims that a public transport system is unsuccessful because they have failed in achieving a particular indicator, without any discussion or examination of the underlying reasons behind such apparent failure. As the research for this paper has found, simply stating that a public transport system is unsuccessful because it doesn't achieve a set indicator rarely means that the public transport system in question is unsuccessful, more often it means that there are other factors at play that influence the success or other wise of the system. This paper looks at the meaning of success in public transport systems, the literature that has attempted to define such a concept, and what factors influence success. The paper explores the public's desire for personal mobility and then examines the available statistics taken from the four cities of Vancouver, Portland, San Diego and San Jose. (a) For the covering entry of this conference, please see ITRD abstract no. E214666.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".