The utterly unforeseen livelihood shock:<scp>COVID</scp>‐19 and street vendor coping mechanisms in Hanoi, Chiang Mai and Luang Prabang
Bibliographic record
Abstract
Well before COVID-19, municipal governments in Vietnam, Thailand and Laos were enacting policies that made street vendor livelihoods increasingly challenging. Yet, vending continues to support tens of thousands of urban households in these three countries. Vendors are often rural-to-urban migrants lacking the formal education skills necessary to secure 'modern' urban employment, and despite ongoing government disapproval, vending provides a relatively low entry-cost opportunity for them to support their household's financial needs. Now add to this complex situation the livelihood shocks associated with the COVID-19 pandemic, as well as additional government restrictions across these three countries to mitigate the pandemic's impacts. Drawing on interviews with 61 street vendors in Hanoi, Chiang Mai and Luang Prabang, and rooted in conceptual discussions regarding urban livelihood shocks, we examine how street vendors, especially rural-to-urban migrants, experienced and responded to the 'first wave' of COVID-19, including additional government-imposed constraints on their livelihoods and mobility. We find that a diverse range of responses helped some-but not all-vendors overcome the initial shocks to their livelihoods and household responsibilities. Yet, we also note that the pandemic's onset altered urban-rural connections and mobility, with many vendors who turned to formerly dependable rural-urban ties for support facing unexpected barriers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".