Overcoming Poverty through Social Entrepreneurship: A Conceptual Paper
Bibliographic record
Abstract
This paper aims to propose a conceptual framework to study the relationship between social entrepreneurship and organizational effectiveness. It also explains various theories of change that social entrepreneurs have pursued in overcoming urban poverty in the country. The study will utilize qualitative methods to collect primary data from social entrepreneurship organization in the main cities in Malaysia. The data from the interview will be evaluated to determine how organizational effectiveness can help social entrepreneurship to overcome urban poverty. Although no single social entrepreneurial venture had put a huge dent in poverty, there certainly have been many initiatives that have notable stories to tell about how they have helps poor people. Many researches are now needed to document which “social” returns on investments and to determine the strategies that lead to the best returns. The findings could benefit not only individual social entrepreneurs but also public, policy maker and firms by clarifying how much social entrepreneurship could be relied upon to help alleviate poverty compared to government and business initiatives. Therefore the findings of this research are expected to provide the view of how social entrepreneurship can give impact to urban poverty in the selected area through organizational effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| 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".