Evolution of money to digital currency
Bibliographic record
Abstract
The purpose of the article is to reveal the evolution of things exchange from its emergence described as a simple model to formation of the commodity-money circulation with its subsequent transformation into an innovative state tending to the digital currency. The objective of the study is to identify the specifics of the double exchange of things model. In it, the needs of one individual are satisfied by the means of ensuring them that are in the possession of another. With the money’s advent, the alienation of one’s product and the appropriation of someone else’s in exchange for it is divided in space and time. This introduces fundamental changes in the simple exchange model. The circulation process does not end like a direct exchange of products. Money does not leave the sphere of circulation. They are deposited at those points in the circulation process that are purified by this or that commodity. The research methodology proceeds from the statements that, firstly, the identical is different, and the difference is manifested in the identity; secondly, in the thing being exchanged there is a hidden contradiction between its subjective assessment for oneself and for the other, and both sides value other people’s things higher than their own. The results obtained reveal the evolution of money towards digital currency as an objective social phenomenon. Their manifestation in different historical periods is compared – random exchange, commodity-money circulation, depreciation of money, the ratio of their supply and demand, the appearance of fiat money, and digital currency – their advantages and disadvantages are revealed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".