Modeling the Risks of the Global Customs Space
Bibliographic record
Abstract
The influence of globalization processes, the customs space of the country, requires the development and implementation of a transparent state customs policy to ensure security and integration into the space of the higher hierarchical order. The purpose of the study is to form scientific-applied recommendations regarding the development vectors of the customs space of a country in the global environment to improve its risk management system. The main method of study is econometric modeling, namely, canonical analysis in determining the interdependence of sustainable development and customs space. The purpose of the study is to suggest directions for development vectors for a country’s customs space that will mitigate various risks. Originally, 174 countries were selected for analysis, but the final sample was formed by 98 countries. According to the results of econometric modeling, it was determined that the following variables have the greatest impact on the customs space: human development index; GDP per capita; corruption perception index; global enabling trade index; environmental performance index; social progress index; global competitiveness index. The findings can be used by public authorities in developing a strategy for reforming the customs system of developing countries, taking into account the risks and challenges of the global environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".