Business Sustainability in Bangladesh: An Analysis of Economic Sustainability in Ready-made Garments Industries
Bibliographic record
Abstract
Sustainable business practices are long cherished system that business organizations are expected to exercise to sustain their businesses. The Ready-made Garments industries (RMG) in Bangladesh are now facing this crucial question to adopt a sustainable system in operating their businesses. The more the industry experiences rapid growth, the higher the demand for the implementation of sustainability. Though the experienced and old garments are seen quite well in maintaining this sustainability, the newly established RMG factories are blamed for not having sustainable business procedure, especially economic sustainability which relates to labor standards and labor rights. Despite continuous tremendous pressure from government and international communities, these new establishments often emphasize on profit maximization rather good labor practices. This article examines the business sustainability issues on economy aspects in new generation RMG factories in Bangladesh. This economic sustainability includes the functional strategies regulating the human resources, labor rights and labor conditions of a factory. To study these issues, this research follows the mixed-method research approaches to get better findings of the sampled factories. The study is based on findings of factory level investigation and analysis of in-depth semi structured interviews and focus group discussions (FGD). The results of factory visits show that most of the investigated factories are lack of implementation of economic sustainability. The findings from interviews and FGD revealed underlying causes of this lack of implementation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".