The Role of Small and Medium Enterprises (SMEs) in Employment Generation and Economic Growth: A Study of Marble Industry in Emerging Economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study is undertaken to find out how SMEs contribute to the economy in terms of employment generation and its impact on the economic growth of the country. Small and Medium Scale Enterprises (SMEs) is accepted globally as a tool for empowering the citizenry and economic growth. In Pakistan efforts have been made by successive governments to increase employment opportunities, reduce poverty and accelerate economic growth by increasing foreign direct investment, diversifying the economy, enacting policy frameworks which favor small business ownership and entrepreneurship programs. Specifically, this study tends to figure out: how SMEs contribute to employment generation, whether a significant number of people is employ within the SME sector; whether the SMEs increase the income level of people. The total number of employees was 255 being selected randomly from Swat marble industries. A questionnaire was constructed and distributed to the selected respondents. The responses were collected and analyzed using the Statistical Package for Social Sciences (SPSS) analytical tool. The study exposes that SMEs play a vital role in employment generation. There is a positive relationship between SMEs and unemployment reduction. The result also shows that there is a positive relationship between SMEs and increase in income level. This study may be beneficial both for practitioners and academicians. For practitioners, the current study may help to devise policies and strategies concerning SMEs to generate employment opportunities. The current study may lead to the generalizability of existing research in the same field as for academic aspect is a concern.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it