Bibliographic record
Abstract
In many cities of the developing world, institutional public transportation is limited or nonexistent, and inhabitants have to rely on paratransit (informal or semiformal, non-fixed-route, nonscheduled transportation systems) for their travel. Although their flexibility and affordability offer clear advantages, these services are often criticized for their lack of reliability in terms of variations in travel time and waiting time. The body of work on paratransit and the work that characterizes paratransit as unreliable are almost exclusively based on self-reported or indirect data. Therefore the aim here is to fill a gap in the paratransit literature by applying concepts from the literature on transit quality of service to the field of informal transport. Indicators traditionally applied to formal transit systems are used to assess the level of reliability of paratransit services in a developing country. In addition, a new indicator is proposed to measure itinerary variations specific to paratransit. It is found that the most appropriate unit of analysis for such research is the station because operations on any given route are influenced by forces at the station level. The general level of variability measured through these indicators was less than expected. Although a wide range of situations was observed in this sample, most paratransit routes appeared to be relatively stable in Accra, Ghana.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".