Bibliographic record
Abstract
Introduction. To determine the strategic goals of transforming the financial system of Ukraine, it is especially important to study the experience of successful and effective world economies. The iconic examples of such systems are the financial systems of Canada and the United States. The purpose of this article is to analyze the dynamics of the United States and Canadian financial systems’ indicators and determine the characteristics of the development of these countries in terms of their future closest financial integration. Methods. The research methodology was based on a combination of such scientific methods as: generalization, graphic and comparative analysis, analysis and synthesis, this made possible to determine the development details of the USA and Canadian financial system and the possibilities for their financial sector further integration and harmonization. Results. The USA and Canadian financial systems are analyzed, especially, the causes and consequences of the financial integration of these systems, as well as possible ways for their further development are thoroughly studied. Such stability indicators of the financial system as inflation, money supply, interest rate dynamics and public debt are researched. Risk assessment of the further development of the financial system of the USA and Canada is also done. Conclusions. The Canadian and US financial systems are closely interconnected through many years of cooperation. Accordingly, the risks in these systems are the same, and factors that are similar for both countries hinder their development. Although, regardless of these factors, in general, the development of the financial systems of the United States and Canada is stable and consistent.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".