Bibliographic record
Abstract
India has witnessed high economic growth rates in the past two decades and there has been a remarkable increase in the per capita income. But unfortunately many sections of the Indian population still remain economically deprived. Disabled persons though constitute a small part of the Indian population but their relative numbers are growing. Disabled lag behind in terms of education and employment which results in poverty. For equitable distribution of wealth and prosperity among all sections of population inclusive growth is necessary. The challenge is therefore not only to achieve higher economic growth rates but also to focus on economic inclusion so that all sections of the society are able to take advantage of opportunities. Promoting entrepreneurship among the disabled is a way to achieve faster and better economic integration. This paper highlights the barriers faced by entrepreneurs with disabilities. Also the paper tries to find out if these barriers are different than those faced by other entrepreneurs. Finally this paper highlights what steps can be taken to prevail over the various types of barriers being faced by disabled entrepreneurs. Keywords: Entrepreneurship, Disabilities, Inclusive Growth, Barriers, Economic Integration
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".