Re-Export: Assessing the Impact of Re-Export Companies on Sectors and the Economy
Bibliographic record
Abstract
Re-exports are an important part of international trade, as they account for about a quarter of total exports, and the growth rate of re-exports exceeds the growth rate of exports. Researchers find that re-exports have a positive impact on economic growth. Despite this, in existing studies, little attention has been paid to the impact of re-exports on sectors of the economy and the direct and indirect effects of re-exports on the national economy. Based on this, the purpose of the article is to consider the impact of re-export activities on sectors of the economy and on the economy as a whole using the example of one company. The study examines the distribution of the effects of re-export companies’ activities between the national economy and foreign economies and among sectors of the economy. In addition, the value of the primary and secondary effects of the influence of the re-export company on the national economy was determined and the local multiplier value was calculated. This study identifies the main factors that influence the distribution of re-export effects between the national economy and foreign economies and among sectors and industries, as well as factors that affect the magnitude of direct and indirect re-export effects on the national economy. The local multiplier value of re-exports was also determined at 1.73.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".