Success by any other name would smell as sweet: different perspectives on success in public transport systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it