Factors that Influence the Sustainability of Poverty Alleviation in Somalia
Bibliographic record
Abstract
Poverty in Somalia is deeply rooted due to prolonged civil conflict, political instability, and environmental adversities. With approximately 70% of the population living in poverty, Somalia ranks sixth in Sub-Saharan Africa for poverty prevalence. The combination of widespread and severe poverty poses significant challenges to socioeconomic and economic progress unless appropriate policies are implemented. The purpose of this study is to identify the factors that influence the sustainability of poverty alleviation in Somalia. In this paper, we examined the impact of four sustainability goals, namely, SDG 2 (no hunger), SDG 4 (quality education), SDG 8 (decent work and economic growth), and SDG 17(social protection) in relation to poverty alleviation in Somalia. A judgemental survey using 130 respondents was conducted using face-to-face (42%) and online (58%) techniques. Descriptive statistics were used to analyze the data and estimate the model parameters. The results found that SDG 2, SDG 4, SDG 8, and SDG 17 are all factors that contribute to poverty reduction in Somalia. Based on these findings, the study suggests that the government and policymakers should invest in sustainable agricultural practices (SDG 2) that not only ensure food security but also create job opportunities and income sources for impoverished communities. The government should also allocate resources to prioritize quality education (SDG 4) to equip individuals with the necessary skills for decent work and economic growth (SDG 8). Finally, the government should focus on social protection (SDG 17) in rural and urban areas, thereby reducing unemployment and providing pathways out of poverty, focusing on various dimensions of social protection interventions.
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 | 0.001 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".