Insights into the Skill Development Issues of Management Jobs: A Study on RMG and Textile Sectors of Bangladesh
Bibliographic record
Abstract
The purpose of this study is to provide an insight into the skill development and training related issues of the management jobs of textile and garments sector of Bangladesh which includes the skills requirements, differences in the skills of domestic managers and expatriates, local training facilities and barriers companies confront while sending their staffs abroad for training. A qualitative research approach has been adopted in this study where data has been collected through 30 in-depth interviews based on convenient and snowball sampling. The findings indicate that certain skills of domestic managers are quite poor like English proficiency, presentation skills, leadership skills, decision making skills. The RMG and textile firms send their employees to Germany, China, UK, USA, Japan and other countries for training but they encounter barriers like visa issues, breach of contract by the employees etc. in this attempt. Government, RMG and textile industries and various trade bodies, educational and training institutions should step up to organize training, develop skill-oriented curriculum to eliminate the reasons of hiring expats. The outcome of this study can be a source material through which HR managers can identify the scarce managerial skills and devise training and skill development programs accordingly not only in Bangladesh, but also in similar developing countries.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".