A Study On Factors Affecting Exports Of Leather Footwear As A Strong Initiative Of Make In India
Bibliographic record
Abstract
“The world economy has become global in nature. Globalization has various manifestations. One of the hallmarks of globalization is the cross-border trade of products and services.India has ample opportunities in the Make in India campaign in the leather footwear sector. The availability of skilled manpower in this sector has motivated investors to manufacture at a large scale. Exports of Leather & Products in India decreased to 33.65 INR Billion in September from 34.05 INR Billion in August 2022.This sector also has the potential to produce approximate six million jobs. Indian leather industry is one of the principal producers of leather footwear globally. The major manufacturing centres in India are Tamil Nadu, Andhra Pradesh, Karnataka, Punjab, Delhi, West Bengal, Uttar Pradesh and Maharashtra. The key objective of this paper is to explore opportunities and challenges in Make in India, different factors affecting exports of leather footwear in the leather footwear sector, the initiatives by the government to boost growth in this sector, and emerging trends in this industry. It also focuses on different challenges that this industry faces. A conceptual overview will be understood based on secondary data and reports available.The observations will help in understanding the scope, improvement areas, and challenges to be circumvented to boost this industry and strengthen our Economy. ”
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".