Indian Rural Entrepreneurship and Industrial Development in Tribal Belt With special reference to Bastar District of Chhattisgarh
Bibliographic record
Abstract
Villages in India have spending power, but they also have some unique problems. What this combination has done is to stoke entrepreneurship among professionals aiming to offer solutions and tap into the rural opportunity. Tweaking technology is also making it possible for startups to offer new applications that suit rural consumers. It is the scale of the opportunity that is drawing scores of entrepreneurs to rural India. Bastar, the tribal district, before splitting into three districts, was one of the largest district in India, with an area of 39114 sq k.m, which was even greater than the Kerala state and some other countries like Belgium, Israel etc. In the year 1999, the district Bastar has been divided into 3 districts namely Bastar, Kanker and Dantewada. All these 3 districts come under Bastar Division with the divisional head quarter at Jagdalpur, which is the district head quarter of Bastar district. The beauty of Bastar district lies in its natural forest area and various types of tribals. The total forest area is 7112 sq k.m which is more than 75% of the total area of the district of the total population more than 70% are tribals like Gonds, Abhuj Maria, Darda Maria, Bison Horn Maria, Munia Doria, Dhruva, Bhatra, Halba etc.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".