War Against Cash Paves Way for Central Bank Digital Currency Which Marks the Advent of Cashless Society
Bibliographic record
Abstract
In the digital age, nations are increasingly paying digitally instead of using cash in this digital revolution. Central Bank of Lesotho (CBL) is considering implementing CBDC in Lesotho. The research study offers a comprehensive view of CBDC world-wide and how CBDC synchronises with Digital ID and Supremacy AGI (Artificial General Intelligence) to determine the credit score of CBDC’s clients. The study further explores the Central Bank Digital Currency, its potential benefits and risks as well as its implications for the future. This study employed qualitative method, interpretive approach and adopted PRISMA. The data was collected through a systematic literature review framework to ensure inclusion and to understand different perspectives in countries that have rolled out the CBDC. Findings reveal that well-developed countries are conducting a pilot study on digital currency while others had launched CBDC. Countries which are already using CBDC include Ecuador, China, Australia, Canada, India, Spain, Italy, Israel, Norway, Nigeria, France and Sweden. Some countries are exploring digital money while other countries are considering using digital currency. Lesotho is also considering using central bank digital currency. The study also reveals the potential benefits and risks of using CBDC from a variety of perspectives. The major benefits of employing digital money is to fight against crime, corruption and tax invasion. On the other hand, it is believed that digital currency is primarily dominated by negative issues especially considering the infringement of freedom and privacy inherent in this future monetary system. The study concludes that the time for digital money through Unicoin has arrived. Therefore, the system of one world currency has begun.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".