Bibliographic record
Abstract
Scam is commonly referred to as an intentional act committed to harm or injure others securing an unfair or unlawful gain.According to the Securities Exchange Act (1934) SEA-"It shall be unlawful for any person to engage in any act, practice or course of action which operates or would operate as a fraud or deceit upon any person in connection with the purchase or sale of a security."There is a certain systemic risk involved if brokers or banks get into settlement problems during the process of transacting in securities.If so, it results in a domino effect, which could create problems for other banks and brokers in the system.The number of past research reveals the various aspects of the securities and financial scams but in this study, an attempt is made to line up the causes which made the financial crisis so grave.The number of provisions and regulations were made to prevent from securities and financial frauds, but still there are some loop holes which causes corporate frauds.Therefore, there is need to analyze the impact of scams on the regulatory framework.The study attempts to find out the causes of these loopholes, as well as the responses of the regulatory bodies on these scams.The objective of the study is to know the impact of securities and financial scams on regulatory framework.A thorough study of the original rules and regulations as well as the amendments made in the rules and regulations of the regulatory authorities due to the occurrence of scams was conducted.This study is descriptive in nature.It attempts to know about the effect of securities and financial scams on the regulatory framework.Therefore, the qualitative analysis of data is done in order to achieve the objective.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.964 | 0.963 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".