EVALUATING GOVERNANCE PRACTICES AND BUSINESS OPERATIONS IN NIGERIA: ATTENDANT RESPONSE TO FRAUD PREVENTION AND RISK MANAGEMENT IN THE COVID-19 PANDEMIC ERA
Bibliographic record
Abstract
Despite the outcome of the COVID-19 pandemic onslaught on global economies that has negatively undermined the financial prowess of major markets in United States of America, United Kingdom, Germany, France, Austria, Canada, Spain, Italy, Nigeria, South Africa, Ghana et cetera, leading to loss of trillions of US Dollars, the need to uphold responsive financial sustainability among nations and businesses has been largely impaired by the prevalence of fraud, cyber crimes, mismanagement of scarce finance resources and external loan funds, bad leadership and poor oversight. Amid unprecedented increase in job layoffs, salary cuts, expenses cuts, robotic technologies embrace, bonuses suspension, promotions postponements, remote working embrace, price hikes, high inflation rate et cetera which have all culminated into heightened fraud occurrences and consequent fraud losses, fraud risk appear not to be proactively considered a top priority by the government and management of businesses in critical challenging COVID-19 pandemic economic downturn period as this. Deploying a descriptive approach, the study expressly evaluates the threat of COVID-19 pandemic upsurge on public governance and businesses with extant emphasis laid literally on attendant responses to fraud prevention and fraud risk management in Nigeria. Conceptualized schematic frameworks and widely recognized current statistics were utilised and evaluated to maximally envisage reliable response measures to fraud risk factors in the contemporary COVID-19 pandemic business environment in Nigeria. The study concludes by proposing possible actions (way forward) for policies development towards rescuing Nigeria and its business environment from the prevailing unfavourable situations..
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".