Bibliographic record
Abstract
Digital mindsets and technology transformation is an inevitable need that organizations, businesses and individuals cannot ignore anymore. Businesses worldwide are gearing up for digital transformation in their existing processes, competencies, models and transactions. Digitization of financial systems and transactions are fallout of this revolution. Bitcoin can also be called a child of this technological revolution. At the onset, bitcoins can be seen as the first pan-global medium of which have been used by people internationally and independently, i.e. without any reliance on government regulations. Of the various forms of digital currencies available today, the current bullish (rather more than bullish) rally of Bitcoin in the year 2016-17, caught my attention and motivated to examine the future of this transaction system, and analyze the two often speculated status of bitcoin - as the currency of the future or an asset worthy of investment, or a mere bubble that will eventually burst. As the most popular form of cryptocurrency (according to research produced by Cambridge University in 2017, there are 2.9 to 5.8 million unique users using a cryptocurrency wallet, most of them using bitcoin) that is used for transactions and can be recorded in a ledger, various conflicting opinion exists regarding its perceived value as the digital currency of the future. In the paper I evaluate how the value of bitcoin is created and examine its potential as a currency of future or a commodity or asset.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".