Socio-economic and demographic differences in the impact of COVID-19 on personal travel in the Global South
Bibliographic record
Abstract
This paper presents the results of a scoping review concerning the state of knowledge with respect to the impacts of COVID-19 on daily personal travel in the Global South. Based on the available literature in the Global South, the paper aims to: (1) provide an overview of the current state of knowledge regarding the personal daily travel of different socio-economic and demographic groups during COVID-19; (2) synthesise the literature to explore the needs of the different socio-economic and demographic groups; and (3) identify groups who received less attention in transportation research in the Global South so far. The paper reviewed 47 studies and found that while investigating personal travel during COVID-19, the most explored socio-economic and demographic attributes were sex, age, income, occupation and educational qualifications. Some regional differences were evident in terms of mode choice during COVID-19. Through the review, it is also noticeable that none of the studies explored LGBTQ+ communities’ and individuals with disabilities’ transportation needs and challenges and how COVID-19 has impacted their personal travel. Other overlooked socio-economic and demographic groups in the Global South whose personal travel during COVID-19 and the post-pandemic period needs investigation are migrant and seasonal workers, children and youths, ethnic minorities, racial minorities, religious minorities, linguistically diverse individuals, indigenous individuals, and individuals residing in rural areas.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".