Turf wars: The livelihood and mobility frictions of motorbike taxi drivers on Hanoi's streets
Bibliographic record
Abstract
In Vietnam's capital city Hanoi, the growing popularity of application based (app‐based) motorbike taxis has offered many inhabitants new opportunities to pursue a mobile livelihood with ride‐hailing platforms. Nonetheless, as this influx of app‐based drivers has hit the city's streets, specific livelihood and mobility frictions have emerged, notably with informal, ‘traditional’ motorbike taxi drivers, orxe ôm. In this paper we analyse these evolving sites and moments of friction and their impacts on driver livelihoods and mobilities for both driver groups. We draw conceptually on debates regarding mobility, platform economies, and urban livelihoods, while specifically interrogating the concept of friction to highlight three possible analytical applications. Methodologically, we interpret static and ride‐along interviews completed with over 130 drivers. We highlight a range of tactics ‘traditional’ and app‐based motorbike taxi drivers have employed to respond to rising frictions, defend their ‘turf’, and maintain their street‐based livelihoods. Driver responses reveal differing access to distinctive forms of social capital and social networks, and contrasting levels of agency regarding their mobilities. By conceptually teasing apart the notion of friction, we wish to expand and deepen understandings of the experiences of vulnerability, precarity, and other impacts of platformisation for different motorbike taxi driver cohorts.
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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 | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".