Transformation from Class Banking to Mass Banking through Inclusive Finance: A Paradigm Shift
Bibliographic record
Abstract
The strategies of rural transformation in India focused on improving the standard of living of rural population has undergone several changes keeping in view the mammoth size of population, its changing dynamics and socio –cultural diversities of our country But the economic stagnation of vast chunk of rural population still suffers from social and financial exclusion resulting with high incidence of poverty, rising unemployment, growing inequality along with rich-poor divide widening. The high growth story of our country in the post reform era is not broad based, regionally imbalanced and socially inequitable Indian Banking industry in conformity with national policy objectives has reoriented from Class Banking to Mass Banking approach to enhance its outreach to bring the marginalized and excluded population into its fold for mainstream economic activity. The branch expansion was accelerated and institutional credit flow to poor rural households reached new heights to relieve them from the clutches of usurious informal money lenders. Modern banking in India has leveraged IT driven initiatives to bring the excluded population into the fold of mainstream banking, develop new products to suit their needs and more importantly educate them on financial products and services to enable them to take informed decisions. The Institutional mechanism, for enhancing access to institutional credit have been strengthened by establishing pro-poor institutions like Regional Rural Banks (RRBs) and National Agricultural Bank for Agricultural and Rural Development (NABARD) etc. The business delivery models like Self Help Groups (SHG)-Bank linkage programme, and Micro finance institutions have been encouraged to provide microfinance services at doorstep of marginalized households. The Agent/Correspondent Banking model of commercial Banks have been given a big boost to provide wide access of low income/marginalized people to Financial Markets. Financial inclusion being a gigantic task, it calls for an appropriate and well-coordinated/collaborative strategy by all the stake holders involved in its implementation. The government needs to increase investment in economic and social capital by increasing investment in basic rural infrastructure. Banking system is poised to provide efficient economic infrastructure like savings, access to easy credit and robust payment system. The community based organizations/NGOs should catalyze local participation to enhance efficiency and transparency in delivery of financial services to common man for making inclusive growth a reality.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".