Active transportation indicators and establishing baseline in a developing country context: A study of Rajshahi, Bangladesh
Bibliographic record
Abstract
Abstract This paper shares the findings of an active transportation (AT) study conducted in the context of a city in a developing country. First, a list of AT indicators was developed based on the literature review and expert opinion survey. Second, a face‐to‐face survey was conducted to collect information on individuals' socio‐demographic characteristics, travel behavior, AT mode choice, and their perceptions regarding the AT conditions in their neighborhoods. Analysis of the survey results suggests that several socio‐demographic characteristics are associated with AT use. For example, young adults and individuals with low income are the primary users of AT. Also, results suggest that students are mainly active commuters. The ratio of AT use increases with the number of bicycles in the household. Individuals tend to walk more when travel duration is less than 10 minutes. Educational areas are perceived as safer and convenient areas for using active modes of transport compared to other land‐use types. Also, a higher proportion of active commuters perceive local roads to be safer from vehicular traffic compared to main roads. Many sectors such as planning, transportation, health, and education as well as non‐government organizations will be benefited from this study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".