Objectives of the Demonetisations in the World. Special Reference to Indian Demonetisation of 2016
Bibliographic record
Abstract
“Demonetisation” means the removal or rejection of one or more legitimate payment methods in the economy. This term is as old as the use of currency, which can be traced back to 7th and 6th centuries BCE. The Indian government conducted an unannounced demonetization in 2016. Similar cases were observed in India in 1946 and 1978. Overall, the world has witnessed dozens of demonetisations in different countries, which were carried out for different reasons. Many were successful, but many were not. We need to understand why demonetisation has been implemented over the world. This study used a literature review method to determine the causes of demonetisation in nations such as Australia, Canada, New Zealand, Libya, Ghana, Myanmar, Zaire, Russia, North Korea, Pakistan, Sweden, Zimbabwe, and Belarus. It also describes the goals of Indian demonetisation in 2016 and determines whether they have been met. As a result, the authors found that pre-announced demonetisation were usually effective, whereas most undeclared demonetisation failed and had an impact on the economy and population. The results of this paper can help governments, policymakers and scientists to understand the purpose of demonetisation and the need for caution. The authors concluded that demonetisation could have both positive and negative effects, depending largely on the intentions of the country’s leadership and on the preparedness for demonetisation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".