Does Entrepreneurial Activity Assist in the Alleviation of Poverty?
Bibliographic record
Abstract
Poverty is a major issue across the world with governments and organizations such as the World Bank and the IMF increasingly looking for ways to reduce its impact. Despite this, almost 2.5 billion people still live in poverty. Entrepreneurial Activity can reduce poverty and can be boosted through the help of Entrepreneurship Facilitators such as Government, Incubators, and Financial Institutions. This study examines the relationship between Entrepreneurial Activity and poverty alleviation using Feasible Generalized Least Square (FGLS). The study found a positive and significant relationship between Entrepreneurial Activity and poverty alleviation as measured by the changes in Human Development Index (HDI) in all 104 countries studied over a 12 year period, and that the presence of good Entrepreneurial Facilitators improves the capacity of Entrepreneurial Activity. It suggests that governments need better business related regulations to motivate entrepreneurs and create ease of doing business.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".