An in-depth study of India's export of leather accessories to asean countries with special reference to leather saddlery and harness
Bibliographic record
Abstract
Trade is not a recent phenomenon. People throughout the world have always wanted to new destinations and buy things made by others. Global trade started liberalization right through the seventies and eighties of the last century. From 4 billion US dollar, global import of leather and leather products increased to an estimated 70 billion US dollar in 2000. Globally, these years also witnessed major structural changes in this sector. Manufacture of leather products such as shoes, garments and assorted leather goods migrated from industrialised countries of the West to the developing countries of the East in a big way, primarily motivated by the consideration of the cost production. The difference in wage levels was too vast to be bridged by any technological improvement, particularly in the light of the labour intensity of many of the leather based industries. ASEAN has become a strategic partner of India and as the business and trade links between the two has been on an all time high nearly one quarter of India's exports is going to the ASEAN and a tremendous scope exists for increasing exports to the ASEAN.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".