Bitcoin as a Global Currency: Exploring the Wild West of Cryptocurrency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bitcoin, and its contemporary substitute cryptocurrencies, are an exciting new evolution in our concept of money. However, there are currently factors holding back Bitcoin, the largest player in the cryptocurrency market, from a wider mainstream acceptance and adoption. The greatest force working against cryptocurrency’s ability to be an accepted method of exchange is its extreme price volatility which cannot be completely attributed to insufficient liquidity (Dyhrberg 2018). This research reexamines several GARCH models using a larger window with more observations than previous researchers, and determine that a GARCH(1,1) with an AR(6) term in the mean equation provide the best fit. After identifying the proper tool, a basket of explanatory macroeconomic variables was tested and further improved the fit. Notably, a strong relationship exists between currencies, commodities, and Bitcoin price variance furthering the common interpretation that Bitcoin exists somewhere in the ether of the two classes. Bitcoin also exhibited significant volatility responses to geopolitical events that imply a use by nefarious state actors. The objective of this project is to gain an understanding of the nature of cryptocurrency and its utilization in the macroeconomy.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it