Bibliographic record
Abstract
Poverty is one of the main issues faced by countries across the world. Over the last three decades, governments and international organizations such as the World Bank, the IMF, etc. have been trying to reduce poverty. Despite this, today almost 2.5 billion people are still living in poverty. Entrepreneurship is often seen as a way to reduce poverty. Moreover, the role of entrepreneurship facilitators is very important in creating a suitable business environment for entrepreneurs which increases the capacity of entrepreneurial activities. The purpose of this thesis is to provide an insight into how entrepreneurial activity and entrepreneurship facilitators (Government, Incubators, and Financial Institutions) help in improving the business environment in all countries and hence in poverty alleviation, examining the impact in case of high-income, high medium-income, medium-income, low-income countries and, as a result, reduce poverty. To investigate this, the Human Development Index (HDI) has been used to measure poverty. Secondary data for Entrepreneurship (Entrepreneurial Facilitators, Entrepreneurial Activities, and Economic Factors) and Poverty (HDI)) from the period of 2005 to 2016 are used for high-income countries, high medium-income countries, medium-income countries and low-income countries. The study has found that there is a positive and significant relationship between entrepreneurial activity and the changes in Human Development Index (HDI) in all countries studied over the 12 years period. It also finds that the presence of good entrepreneurial facilitators improves the capacity of entrepreneurial activity which reduces poverty as measured by the HDI. It adds to the body of knowledge by using HDI as a new tool to analyze the impact of entrepreneurial activity country wise. It also suggests that governments need to make better business related regulations which will motivate entrepreneurs and create ease of business doing. Finally it suggests that trade openness bring foreign investments in a country which create employment for people.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".