LDC Graduation of Bangladesh- In Search of Coping Strategies for the Bangladeshi RMG Industry
Bibliographic record
Abstract
This paper set out to assess the implication of graduation on the RMG sector of Bangladesh and propose coping strategies for smooth transition. It has been identified that Bangladesh has a RMG export concertation (80%) in the EU and North American market, a product concentration of 73% in 5 basic products, a dependence on cotton for 74% of RMG exports and a very poor global competitiveness index rank (105th out of 141 countries). Graduation from LDC category will mean that Bangladesh will be subject to standard Generalized System of Preferences. Bangladesh would also lose Duty-Free and Quota Free access to EU, Canada and other developed countries. Furthermore, RMG exports from Bangladesh will be subject to normal Rules of Origins, entailing the erosion of the “single transformation” charge they were once entitled to. Hence, to ease transition and to take full advantage of graduation, Bangladesh should expand its international market for RMG exports by targeting markets it has condoned till date. These include China, India, Indonesia etc. To widen its portfolio and reduce its dependence on cotton, Bangladesh should invest in Man-Made Fabric (MMF) and high-tech products. To this end Bangladesh should attract FDI to utilize the potential of backward linkage in MMF. Attracting FDI will prove to be easier if Bangladesh can improve its competitive indicators by investing more in infrastructure, industrial upgradation, administrative hurdles etc.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".