Bibliographic record
Abstract
COVID-19 and Bangladesh analyzes the aftermath of the COVID-19 pandemic and features the socioeconomic fallouts for disadvantaged communities in Bangladesh, their coping mechanisms, and implications for the country’s development ambitions. The contributors to the book examine the immediate impact of economic adversities, which rapidly translated into health, employment, education, and other socioeconomic problems. They show that the pandemic has disproportionately impacted the communities that were traditionally left behind and created a new group of people that are “pushed behind”. Structured in four sections, the book examines impact and adjustment in the areas of employment, income, and expenditure and health, education, and the Sustainable Development Goals (SDGs) and offers policy perspectives. The empirical analysis and policy conclusions presented in the chapters are based on official secondary data, household-level primary surveys, focus-group discussions, key informant interviews, and reviews of public policy documents. The policy conclusions and outlook presented in the book can be instructive for other low-middle income, or graduating least developed countries (LDC). A unique contribution to the current debate on the diverse implications of the COVID-19 pandemic, this book will be of interest to policymakers and academics studying health and society in Asia and other countries of the Global South.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.009 |
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".