Comparative analysis of the impact of the uncertainty factor on the growth rates of the economies of a group of countries dominating the global economy in the period 2013–2023
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
The relevance of the research is determined by the need to search for effective models of economic development in conditions of uncertainty. The study aims to conduct a comparative analysis of the development of the economies of developed and developing countries in a period of uncertainty in order to identify the characteristics of their response and adaptation, as well as to identify measures that help maintain their resilience to the negative impact of emerging challenges. The objectives of the study are to conduct a comparative analysis of indicators of socio-economic development of developed and developing countries in times of uncertainty; to identify the specifics of their response and adaptation to the challenges and threats of uncertainty; to identify ways to maintain the stability of economic systems in times of uncertainty. The present study was carried out using the methods of economic and statistical analysis, comparative analysis; graphical method; abstract-logical method. The article provides a comparative analysis of the socio-economic development of the G7 and BRICS countries, which revealed an increase in inflation in response to the challenges of uncertainty in the economies of all the countries studied, to a lesser extent in China and Japan. A study of the dynamics of general unemployment showed the worsening of structural problems in the labor markets in the G7 and BRICS countries, as well as the effectiveness of employment promotion policies in China aimed at increasing production and entrepreneurship. The growth rate of gross fixed capital accumulation in the BRICS countries has remained positive since 2021, and the trend is growing in many countries, unlike in the G7 group of countries with a predominantly slowing trend. The consequences of the challenges of uncertainty for both the economies of the G7 countries and the economies of the BRICS countries are presented in the form of a slowdown in economic development. However, the USA, Canada, China and India demonstrate great flexibility and adaptability of their economies to the changes in terms of GDP growth rates. The article offers the author's recommendations on the implementation of public policy aimed at increasing the level of adaptability of economies to conditions of uncertainty. In a period of uncertainty, the economies of developed and developing countries are vulnerable to the threats of inflationary processes, destabilization of labor markets and a decrease in investment activity, which in the long term becomes a factor in slowing economic growth and reducing the well-being of the population. The authors declare no conflicts of interests.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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