Understanding the Operation of Motorcycle Taxi Drivers in Nigeria Using Causal Loop Diagram
Bibliographic record
Abstract
Road safety is a subject of concern the world over and many studies have looked into how to improve safe travel. Motorcycles, including motorcycle taxis, are particularly vulnerable. This paper reports the outcome of a study conducted on motorcycle taxi safety problems using a system dynamics method. Qualitative data was obtained from the field and analysed using qualitative analysis methods. The outcome of the qualitative analysis led to the formulation of a dynamic hypothesis for a system dynamics approach, whose first step was to develop and analyse a causal loop diagram [CLD]. This CLD demonstrates how deterrence, a behavioural pattern that can be produced by the appropriate application of sanctions, is both strengthened and weakened within the system. The paper uses this analysis to provide insights about the behavioural patterns of motorcycle taxi operation in Nigeria. These insights include the possibility of maintaining the system at equilibrium for a desired level of deterrence as well as the possibility of breaking undesirable cycles of bribery and jumping arrest loops. These insights can also be useful in other countries of the world where motorcycle taxis operate.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".