To Combat White Collar Crimes In Public And Private Sector And Need For Strong Legislation And Ethics
Bibliographic record
Abstract
The paper deals with and investigates the requirement of resilient and strong legislation to combatwhite collar crimes, which are exponentially increasing with time both in public and private sectororganizations. The influence of immoral and illegal practices in government, particularly the bribesof the highest order has paralyzed the normal functioning of the financial and legislative organizations.Bank frauds and cybercrimes are also increasing tremendously by employing digital devicesand the internet. The purpose of this research is also to investigate the essence of the application ofstrong laws as well principles of morality and ethics. It is advocated by the authors of this paper thatnorms of morality and ethics on one hand impact a healthy effect on reducing such crimes but alsoenhances the productivity and profitability of the organizations in financial terms. Several modernorganizations are incorporating and implementing the codes of ethics in professional practices.More strict must be the legislation and Code of Ethics, Ethics is the deterrence force to discouragethe wrongdoings and adopting right approach so that honesty prevails and curses, crimes, offensesand sins can be minimized. There a serious need of introducing the social welfare system adopted inEurope, Canada and the USA. The bank employees are involved in Bank frauds and commit offensesof embezzlements and illegal transitions using the computer and other digital devices. The role ofbusiness ethics in the banking sector is of immense importance. The malpractices by some politiciansmust also be noticed with stern and iron hands.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".