DETERMINING TARGET MARKET OF IRAN'S PISTACHIO AND DATE EXPORT USING EXPORT DEMAND MODEL AND SCREENING METHOD
Bibliographic record
Abstract
The general objective of this paper is to recognize factors affecting the target market of Iran's pistachio and date export. For this purpose, the target market of pistachio and date export(with use screening method), effective factors on export demand of pistachio and date, and elasticity coefficient of export demand of pistachio and date were all analyzed during 1971-2007. According to the results, Non-Asian countries with high income are suitable markets for exporting Iranian date. Also, Asian and Non-Asian countries with high income are suitable markets for exporting pistachio of Iran in long run. Increasing the date price must be stopped to assist promotion of hygienic level and packing export date. Increasing pistachio price with regard to the appearance of new exporters is hazardous. Generally, according to findings, suitable *Head of Support Policy, ARDPERI, Tehran, Iran (Corresponding Author) **MS in Agricultural Economics e-mail:royamohammadzadeh@yahoo.com ***Researcher in ARDPERI, Tehran, Iran هعسوتو يزرواشكداصتقا لاس ه ج مهد هرامش، 70 20 markets for exporting Iranian date are proposed France, United Kingdom, Germany, Morocco, United State of America, Italy, Spain, Canada, Russia, Georgia, Switzerland, Netherlands, Australia and Belgium, and suitable markets for exporting Iranian pistachio in long run are Luxemburg, Hong Kong, Germany, Spain, Italy, Netherlands, France, Russia, Jordan, India, Syria, Slovakia, Palestine, United Kingdom, Cyprus, Ukraine, Slovenia, Belgium, Latvia and Armenia respectively. Jel Classification: M31
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".