Pedals and throttles: Ride‐along experimental journeys with Hanoi's cyclo and motorbike taxi drivers
Bibliographic record
Abstract
Abstract In this article, we analyse the effectiveness of ride‐alongs, a specific mobile method, to better understand the daily realities of informal mobile livelihoods in Hanoi, Vietnam. The field of mobile methods has seen significant advances both within and beyond geography. Yet, there is still an absence of literature comparing the benefits and drawbacks of using a consistent mobile method across different forms of mobility in the same context, such as pedal‐powered versus motorised transport. Additionally, studies specifically addressing the daily experiences of informal cyclo (trishaw) drivers in Vietnam are scarce. Our paper aims to fill these gaps by evaluating the effectiveness of ride‐along interviews in understanding the mobility and livelihood challenges faced by informal cyclo and motorbike taxi (xe ôm) drivers in Hanoi, who navigate the city's dense and chaotic traffic to earn a living. Ride‐alongs provide a unique perspective on the city's informal transportation sector, uncovering new insights into the nuanced micro‐mobilities and rapid decision‐making required of these drivers. Cyclo drivers navigate Hanoi's streets with considerations for tourist appeal, physical exertion, and police avoidance. Meanwhile, xe ôm drivers manoeuvre through alleyways and roads, balancing efficiency, speed, and passenger demands. Both groups are concerned with circumventing often‐corrupt police, managing local traffic conditions, and adapting to changing weather patterns. This comparative study reveals the benefits and insights gained from ride‐along interviews with mobile informal economy workers, highlighting the similarities and differences in the choices and tactics these drivers employ. The method allows for a deeper understanding of how vehicle type, physical demands, and the socio‐political environment shape the split‐second decisions these drivers must make to maintain their livelihoods on Hanoi's streets.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".