Bibliographic record
Abstract
Despite the fact that today, most developing countries are trying to bring their economies closer to a free economy and in this way, bring themselves together on the world stage, but there are also countries that consider subsidies and assistance to the domestic industry as one of the important ways to further support domestic industries.In fact, these credits have turned into investment incentives and help the economy and employment and capital of the country, increase social welfare and can affect the company's manufacturing and export activities as well.Therefore support for the export industries, especially non-oil exports, is one of our country's priorities for increasing economic growth and creating foreign exchange earnings.This research has been designed with the aim of investigating the relationship of subsidy on export development in the country, so that export can be improved by employing appropriate subsidies.This study is applied from purpose point of view, in the descriptive-survey framework and has importance on the field.The questionnaire was designed in Likert scale & distributed among 261 Managers and experts of the company export in Iran.Cronbach alpha is calculated as %86, which is well above the minimum desirable limit of 0.70.The study investigates 18 factors & extracts five important ones, which profitability and economic growth, quality, developmental factor, The Functional -Financial aspect, the incentive aspect.In this paper for analyze the data use from spss softwares.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.936 | 0.933 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".