The Effects of Money Laundering on Monetary Markets Introduction
Bibliographic record
Abstract
The aim of this study is to determine the impact of money laundering on: the competition in the financial and monetary markets, the stability of the investment sector in Jordan, the ability of the government to control the monetary policies in Jordan, the attraction of foreign investments to the markets in Jordan and the Jordanian dinar exchange rates. A survey of the components and sample of the study population, composed of employees of the Central Bank of Jordan and the Audit Bureau, was used. A questionnaire was developed as a tool for the study, distributed to the study sample of (35). The study concludes that: Money laundering has a massive effect on monetary markets, competition in the financial and monetary markets, the government’s capability to control monetary policies, the investment sector’s stability, attracting foreign investments for the marketplace, and Jordanian Dinar’s exchange rate. Depending on the results of the study, the researcher recommends Introducing laws and regulations to combat money laundering, fostering the role judicial authorities, empowering prohibiting and punishment of involved financial institutions, confiscating of funds, punishing perpetrators, and developing legal procedures that regulate banks’, financial institutions’ and companies’ activities.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".