Investigating the Policy of International Trading Companies of Iran in Using Letters of Credit
Bibliographic record
Abstract
Letters of credit play a crucial role in international trade. In this study, we investigate the most preferable characteristics of letters of credit for the international trading companies in Iran. We seek to investigate which kind of guarantee (commercial L/C or standby L/C) they prefer to secure the performance of their client's obligations. Studied companies were 20 international trading companies in Tehran, from which 50 experts were participated in our survey study. For measuring the participants and their policy in using letters of credit, a questionnaire in Persian was designed. For analyzing data we used statistical tests (One-sample t-test). Data showed that being a guarantee, integration, documentary conditions, and secondary payment mechanism are four important reasons for international trading companies in Iran to use letters of credit in their contracts. Statistical results showed that “being a guaranteed credit” is the most important factor with a mean of 3.660 ± 1.135.Also, we found significant relationship between these four factors and the L/C usage policy (p <0.01, and p< 0.05). We concluded that they mostly prefer standby letters of credit for securing their transactions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".