Principal Components-based Investigative Study of Counter Measures to Financial Crimes
Bibliographic record
Abstract
Financial crime is presently a very serious threat to the global, regional and national economies as it manifests itself in several financial institutions and government agencies. One of the reasons for the thriving status of the menace has been lack of globally acknowledged counter measures. Based on this, twenty eight indices that were considered to be related to financial crimes were formulated in this research. A questionnaire was formulated based on the indices and administered on selected financial related agencies and parastatals in States across Nigeria to obtain relevant data which were subjected to Principal Component Analyses (PCA) using SPSS. The analyses showed that effective citizens’ control and monitoring, economic and social stability, moral suasion and political security and goodwill are the much desired counter-measures to financial crime. The percentage of relevance of these measures was 64.17%, indicating that the indices of some extraneous counter-measures were not given consideration in the research. Such measures include but not limited to good governance, financial security and moral training. A coefficient score matrix was also generated for the estimation and ranking of the contribution of every respondent to the extracted counter-measures.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.001 | 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".