Impact of Bitcoin as a World Currency
Bibliographic record
Abstract
In an era of technology advancement when the entire world is talking about the “Internet of Things” whereby we are expected to have connectivity between anything and everything, Currency cannot be left behind. Paper currency is bound to be a thing of past, as virtual currencies will start taking over and Bitcoin is well poised to achieve this feat. Not only it will revolutionize the way payments are made, but also have potential to impact the future of world currencies like USD, which is already facing challenges from EURO or Chinese Yuan Renminbi (CNY). The rise of crypto-currencies will add a new dimension to this challenge for US Dollar (USD)The focus of this study is to understand multiple factors which are translating Bitcoin (BTC) that is gaining momentum in various fields of global finance and how disruptive it can be, including replacing main fiat currencies in the financial system impacting mainly USD. The key variables studied are Regulation or lack of it around Bitcoin, Bitcoin Technology, Bitcoin Economy and the usage of Bitcoin as a Currency. This research used the latest statistical tool ADANCO 1.1.1 by Henseler and Dijkstra (2015) to analyze the data collected by building a partial least squares structural equation model (PLS-SEM). The observations of this study will help understand the future of global finance from multiple standpoints, especially Regulation, Cryptocurrencies and the fiat currencies.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".