Assessing the Policy of Attracting Investments in the Main Sectors of the Economy in the Context of Introducing Aspects of Industry 4.0
Bibliographic record
Abstract
The purpose of this work is to study and evaluate the impact of world commodity prices on the dynamics of investment in exporting countries of natural resources using the developed methodology under the influence of industry 4.0 aspects. Modern economic activity is accompanied not only by the impact of COVID-19, but also by the impact of the first manifestations of industry 4.0. This applies not only to export-import operations but also to the very need for them due to the cost of new technologies. Using mathematical methods, we investigate the impact of world commodity price indices, in particular, the general commodity price index, the agricultural commodity price index, the food price index, the metal price index, and the crude oil price index, on the dynamics of investment in commodity-type economies in both dimensions – level and volatility. The innovativeness of the study lies in determining the significance of the impact of world commodity prices on the dynamics of foreign direct investment (FDI) of raw material exporting countries (on the example of three groups of countries with different levels of economic development). The proposed methodology makes it possible to empirically evaluate the mechanisms of the macroeconomic impact of commodity prices on investment dynamics.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".