Impact of Small and Medium Enterprises SMEs on Rural Development in Sindh
Bibliographic record
Abstract
This research investigates the impact of Small and Medium enterprises (SME’s) in economic development in general and with reference to Rural Development, in particular, to analyze the export potential of Pakistani’s SME’s and its impact on the economy of Pakistan. Data were collected from 100 SMEs business exports in Pakistan by using simple random technique and Structural questionnaire is the basic tool for measuring export potential. Data from developing countries were collected through secondary sources and data were analysis by using Gen-Stat-statistical software. The response rate was 90% 180 samples were responded in this research. Gen-Stat statistical software has been used to analyses the data. It was revealed that the export potential of SME’s in Pakistan is much better than among third world countries but compare to developing world we are bit slow in developing export markets in the world. The strategic planning and resources should be needed for increasing export through SME’s. The results showed that Pakistani’ SME’s using only small portion in the export of the SME’s products where as other developing countries like Malaysia, Thialand, Japan they develop their economy through SME’s. A survey was conducted in various districted of Pakistan and specially 60% from the interior Sindh province. Sample size was 200 and using simple random technique. The growth of SME in Pakistan is increasing in last decades and especially in the interior Sindh the women’s are more actively participate in developing Small and Medium Enterprises business in Sindh. It was revealed the Government and private sector should encourage women’s to participate more in developing SMEs in various parts of Pakistan.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".