Bibliographic record
Abstract
The paper considers financial technologies that were used in the time of Peter the Great to organize non-cash payments abroad. The sources for the study were legislative documents, letters of Peter the Great’s contemporaries and mentions of historians about financial calculations in Peter the Great’s time. The author considers the use of promissory notes and bills of exchange that were used in the time of Peter the Great to organize non-cash payments abroad. Russian merchants used them in trade settlements in Arkhangelsk long before Peter I. During the reforms of the first quarter of the XVIII century notes and bills settlements were carried out by Russian people on the territory of Europe already. This explains the wider use of the notes and bills in both interstate and private settlements, including the purposes of obtaining scholarships for young people sent abroad by Peter to study. Peter himself and his pets used all the means of payment available at that time in Europe – both cash (gold and silver coins) and non-cash means (promissory notes and bills of exchange). An important issuer of bills of exchange for Russians was the Amsterdam bank, since Amsterdam was the center for the sale of Russian goods. Not only trade transactions, but also the payment of royal orders in different countries, and the issuance of stipends to scholarship holders, took place by transferring bills of exchange from the Amsterdam bank or Amsterdam merchants accepted to other European cities of Europe. In addition to mastering the ‘basic’ profession, those staying abroad needed to show some financial literacy (which could not be obtained at home), skills in handling modern (for that time) securities, and visit banks from time to time. Russian students were prevented from studying not only by the possible temptations and pastime of a beautiful life abroad, but also by serious life difficulties that arose in connection with the financial crisis in France in 1720-21.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".