Financial Inclusion in Developing Countries: Evidences from an Indian State
Bibliographic record
Abstract
Financial Inclusion poses policy challenges on a scale and with an urgency that is unique for developing countries which house more than 90% of the world’s unbanked population. Developed countries policy makers have recognised that there are complex and multi-dimensional factors that contribute to financial exclusion and therefore require a comprehensive variety of providers, products and technologies that best suits the socio-economic, political, cultural and geographical conditions in these countries. India’s experience as a developing country towards ensuring financial inclusion and weeding out financial exclusion has been unique. Indian economy has achieved a phenomenal economic growth during the last decade or so. But this growth has not been inclusive. Mobile phones, E-Mail, E-Commerce, Swanky Cars, trendy dresses, plastic money and 24-hour banking through ATMs have all become a reality in the country but only in the cities and towns. One of the reasons for this exclusive growth witnessed in the country has been attributed to the failure of the second generation reforms which were broadly related with financial sector reforms aimed to achieve greater financial inclusion. The problem of ‘Financial Exclusion’ is severe in the country. The State of Jammu & Kashmir is no exception to this socio-economic problem. An effort has been made in this paper to assess how serious the problem of financial exclusion is in the state? What has been the impact of the initiatives taken by RBI towards greater ‘Financial Inclusion’ & what more needs to be done to achieve full & meaningful financial inclusion?
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".