Case Study of Quelimane, Mozambique
Bibliographic record
Abstract
Bicycle taxi is a vital means of informal public transport service in most Sub-Saharan African cities, and for this reason, understanding who operates this service, and how they operate could help define initiatives to promote this service. This study considered clusters of bicycle taxi operators and their main service operation patterns. A survey was conducted among 105 regular bicycle taxi operators in Quelimane, Mozambique. Twostep cluster analysis identified homogeneous groups of bicycle taxi operators based on six socio-economic factors (age, income, education, household composition, bicycle ownership, and residence location). A Mann-Whitney U test was employed to compare pairs of clusters of bicycle taxi operators regarding a set of taxi services operation variables, such as the number of passengers carried daily, daily revenues, and service hours. Four clusters of bicycle taxi operators were identified which are, less-educated operators from large households (C1), educated migrants (C2), less-educated bicycle renters (C3), and young cyclists from small households (C4). When comparing differences in service operation patterns per cluster of bicycle taxi operators, the study showed that people in C1 produced fewer bicycle taxi trips than those in C2 and C4. For daily earnings, people in C2 earn more than those in C1 and C3. For service hours, individuals in C2 cycle long service hours when compared to those in C1, which could be harmful to their health. The result of this study could reorient bicycle taxi service promotional policies to make the service more sustainable.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".