Bibliographic record
Abstract
This study investigated financial crimes in Somali public sector. It intended to explicitly assess public workers’ perception of financial crimes, as well as the primary causes and effects of financial crimes in Somali Public Sector. A descriptive research approach was used in this study, and a questionnaire was used to gather data from 160 participants. This research was guided by the Fraud Triangle Theory and Fraud Dimond Theory which describe pressure, opportunity, rationalization and capability as key factors for conducting financial crimes. Although these elements have a significant influence, the findings showed that opportunity mainly representing improper internal audit and control, poor governance and improper duty segregation is the most contributing element to financial crimes in Somali public sector. The findings also revealed that financial crimes disturb resource allocation, wealth distribution and socioeconomic development, resulting in poverty and loss of public trust in government institutions. The study concludes that the financial crimes in public sector of Somalia is alarming and is affecting the economy, quality of life, wellbeing, integrity and social progress. However, this study recommends that the Somali government should establish effective control mechanisms, apply appropriate budgetary strategies to ensure government financial soundness and establish/activate government anti-corruption bodies to combat any form of corruption and financial crimes. Furthermore, the government should develop strong legal frameworks to promote accountability and deter perpetrator. The research also recommends that the government implement e-government with the purpose of increasing transparency and public trust. Finally, the study recommends that international donors should help Somalia to develop strong public institutions by providing administrative and technical support, particular, public financial management system.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".