Women's Contribution to Trade in Bangladesh and Challenges in Post-COVID-19 Era
Bibliographic record
Abstract
Abstract New trends in global trade including rise in services, global value chains, and the digital economy are opening up important economic opportunities for women. Trade has the potential to expand women's role in the economy, decrease inequality, and expand women's access to skills and education. Trade can dramatically improve women's lives, creating new jobs, enhancing consumer choice, and increasing women's bargaining power in society. In Bangladesh economy, the women led micro, small, and medium enterprises (MSMEs) play a noteworthy role by providing services and goods, creating employment generation particularly for women (UN Women, 2020). According to an ILO report, the majority of female-owned SMEs in Bangladesh are involved in the trading sector, followed by the manufacturing and service sectors (Fatima, 2023). This chapter is based on the case studies on 50 women entrepreneurs in various levels in Bangladesh and 10 key informant interviews of government officials, business associations, academics, researcher, microcredit organizations. This is encouraging that due to government's women friendly policies and organizational supports along with better networking through social media in Bangladesh, more and more women of various backgrounds in Bangladesh are coming to business though still concentrated on few traditional areas but they are making space for themselves and creating employment for poorest segment of women and educated young women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".