COVID-19 Burdens on Livelihood Opportunities: A Study of Easy-Bike Drivers in Rangpur City, Bangladesh
Bibliographic record
Abstract
This research explores the nexus between COVID-19 and the livelihoods of easy-bike (three-wheeler human hauliers) drivers using a case study of Rangpur City, Bangladesh. Although easy-bike has become a prevalent form of paratransit among city-dwellers in medium-sized cities in Bangladesh, many passengers are now avoiding such paratransit to maintain health and safety guidelines during the COVID-19 pandemic. The pandemic has negatively affected easy-bike drivers’ income in many medium-sized cities. To conduct this study, we collected primary data from the field, with the health and safety guidelines recommended by the government of Bangladesh in consideration. The results demonstrate a decreasing number of trips due to government policy changes under the COVID-19 pandemic, influencing people’s earnings associated with this transit system. We summarized the data to capture the attention of policymakers, who may need to introduce any foreseeable action to assist workers of different professions in need of economic assistance in cities outside of the capital city in Bangladesh. Moreover, we suggest the need to consider these urban transport workers as a vulnerable group for livelihood assistance within the country.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".