Comparative assessment of export competitiveness
Bibliographic record
Abstract
The focus of the book is: firstly, to examine the export competitiveness of textile and apparel products of Bangladesh and Malaysia, using economic indicators like Revealed Comparative Advantage (RCA) and Constant Market Share (CMS) analysis. The RCA calculations clearly show an increasing strength of comparative advantage to Bangladesh for the export of textile as well as apparel products. In the case of Malaysia, the RCA calculations also show an increasing strength of comparative advantage of the export performance ratio for the export of textile and apparel products. Regarding the export growth of selected textile and apparel products for both countries, the CMS results demonstrate that, in most cases, Malaysian shows better competitive performance than Bangladesh. In the case of apparel products, both countries show the negative export performance of commodity composition and market distribution effects. CMS analysis depicts that the competitiveness effects of both countries for these apparel products subsequently improved in general during the period 1992-1996 as compared to those periods of 1982-86 and 1987-91. The results of the shift-share analysis indicate that market opportunities for the export of textile and apparel products in Bangladesh are mainly offered by the USA, Canada, and EU countries. On the other hand, the USA, Canada, and EU countries have also been identified as potential markets for Malaysia for textile and apparel products.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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".