Bibliographic record
Abstract
Migration and international trade are two important dimensions of globalization. Migration plays an important role in development of countries. Immigrants send their remittances, ideas, innovation and investments to their home countries. Migrants can influence on countries’ trade, they are able to decrease the transactional costs for companies willing to trade. In this article has been tried to study the case of Iranian immigrants in Russia. We can see that Iranians have migrated mostly to developed countries such as USA, Europe, Australia, Canada and part of them have migrated to the Persian Gulf countries. And of course many of these immigrants have high levels of economic, human, social, and cultural potential, which can be used for social and economic development of the country. Iranians have migrated to two kinds of countries. First, those who are developed and second those with high income which have the potential of trade with Iran. When we look at these two groups they either migrated to American and European countries, which this group has a high educated and human capital background or they migrated to neighbor Persian Gulf countries that they have mostly strong economic backgrounds which increased the chance of trade. In this article Iranian businessmen have been interviewed and they have explained their roles in trade, and if they had any advantages in comparison with those in the home country.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".