Frequency of Trade Obstacles for India’s Micro, Small and Medium Enterprises
Bibliographic record
Abstract
The Micro Small and Medium Enterprises (MSMEs) have emerged as the major export driven markets in India. The constant improvement of MSMEs exports and its contribution to GDP for last two decades was most significant. The MSMEs performance not only in production and export growth but also in the employment generation was appreciable. It results further the MSMEs capability has increased to compete in the international markets. But, the MSMEs sector attempt to internationalyse, often encountered trade obstacles. Therefore, this paper identifies the frequency of trade obstacles imposed by the major countries on the specified products. The results of the analysis revealed that exports subjected to affect trade barriers was high in the products of food & allied products, cotton & textiles, chemicals, industrial goods, engineering and electrical goods. The countries such as USA, EU, UAE, Japan, Canada, Australia and Turkey were likely to impose trade barriers more systematically. The highest frequency of trade barriers on MSME products were standards followed by customs, import restrictions, labeling and anti-dumping. Therefore, it is essential to eliminate the existing trade obstacles and sustain the business environment that reinforces the international competitiveness of MSMEs. Keywords : Micro, Small and Medium Enterprises, export growth, gross domestic product, employment, international markets, trade obstacles.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".