MULTI FACETED WHITE COLLAR CRIME EATING COUNTRY'S ECONOMIC FABRIC LIKE TERMITE
Bibliographic record
Abstract
White collar crime is the term used to describe financially driven, nonviolent crimes committed by private or holder of public office in business and by government functionaries. Securities fraud, embezzlement, corporate fraud, cheating public at large and money laundering are a few examples of white collar crimes. A nation's legal system's primary goal is to provide justice to all of its residents without distinction. But it is true that prejudice exists in all legal systems. These are the systemic gaps that called for investigation. In our culture, a crime committed by someone of lower social strata is normally punished harshly by the law, but if the same crime is done by someone of high class status, the situation changes. In practice, it appears that the law—or perhaps I should say the system—is more forgiving of the perpetrator. New issues appeared in every industry as civilization expanded and evolved, and the criminal justice system was no exception. In this course, the concept of "white-collar crime" also emerged. The social scientist Edwin Sutherland, who was the main proponent of this social perspective of crime, is credited with creating this word. According to Edwin Sutherland, a specialist in criminology, white-collar crime is: "A crime committed by a person of respectability and high social status in the course of his occupation" (1949) It addresses criminal behavior by holder of public office, those in positions of authority, and even people connected to for-profit businesses. Since white-collar workers have easier access to chances for fraud, bribery, insider trading, embezzlement, cyber crime, and forgeries, white-collar crime intersects with corporate crime. White-collar crime has a big impact on a country's economic and financial stability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".