The Role of Industrial Incentives in Development of Small and Medium Industries
Bibliographic record
Abstract
Small and medium industries have had a significant impact on creation of new employment opportunities in recent years. In other words, in today’s modern world, small and medium industries have a significant role in variables like economic growth, competitiveness, and resolution of the ubiquitous unemployment crisis. Even though large-scale industries are still important in economic policy-making due to their economy of scale, the effect of massive production, experience, and organization, small and medium industries have some advantages including transportation, market size, regulations, choice effectiveness, and control. Accordingly, these industries have become the first choice for the production of most goods. This study investigated the role of industrial incentives in the development of small and medium industries in East Azerbaijan province. Based on the review of articles, texts, relevant sources, and comments from experts, relevant indicators were defined for each component of industrial incentives. Based on these indicators, a questionnaire was developed with 30 questions. After testing the validity and reliability of the questionnaire, it was applied to statistical samples using stratified random sampling. Then the questionnaires were collected and the obtained information was analyzed using descriptive statistics as well as inferential statistics (Spearman correlation test r and step by step Regression). The results show that there is a statistically significant relationship between the variables in this study. and obtained Results of regression Analysis shows that across 4 indicators Industrial Incentives, 2 indicators of it, that is, Financial and Customal Incentives were appropriate predicators for Development of Small and Medium Industries.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".