Impact of Demographic Features of Young Entrepreneurs on Financial Literacy: Meta-Analysis in Delhi NCR
Bibliographic record
Abstract
Financial literacy capability impacts individuals, families, financial institutions and the economy, as a whole. Financial knowledge is required in every aspect of life in this competitive world. The fundamental object for this analysis is to assess the impact of demographic features of budding young entrepreneurs on their financial Literacy, in the national capital reason of India. The methodology used is, the self-administered and closed-ended questions to collect the experiential data from the young entrepreneurs. Before conducting the survey, dense literature was conducted to understand the background of the concept and to find the research gap. The study reports that a young male budding entrepreneur in the age group of the '20s, started a venture or entrepreneurial activity at an early age and graduate in the field of technology, accounts, economics or management is well versed in understanding the financial implications. The socio-economic culture of India supports the results. The outcome of the study will be useful for the banks, financial institutions, venture capitalist, who involved in financing the start-ups and new ventures moreover, it is also essential for government and policymakers who gives subsidies and other support to make India as a self-reliant nation "Aatm - Nirbhar Bharat".
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.020 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".