Identifying the Barriers to Iran’s Saffron Export by Using Porter’s Diamond Model
Bibliographic record
Abstract
Saffron is an important export product of Iran. Saffron role as an agricultural export product is now obvious worldwide. It is important to identify the barriers to Iran’s Saffron export in order to maintain Iran’s position as the world’s biggest producer and exporter of saffron. The purpose of this study is to determine the barriers to Iran’s saffron export to international markets using Michael Porter’s Diamond Model.The type of this paper is empirical and practical and the data collection method is descriptive-cognition. The related information for this scope have been collected by using library resources such as books, scientific journals and moreover, in order to accept or reject the research hypotheses a questionnaire with 42 questions made by researchers have been used. The statistical society of this research includes all the managers, advisors and experts of Iranian saffron export companies.All the hypotheses of the research were analyzed at the 95% confidence level. The results show that the most important barriers to Iran’s saffron export include the demand conditions, related and supporting industries, firm strategy, structure, and rivalry, government, and chance. The results also indicate that factor conditions are not important barriers to Iran’s saffron export.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".