Decoding the Great Indian Recapitalisation Plan: Restoring the Health of Public Sector Banks in India
Bibliographic record
Abstract
Indian government has infused `250,000 million in the year 2016 and 2017 followed by `100,000 million within the year 2018 and 2019 with an inspiration of reducing the non-performing assets (NPAs) levels of public sector banks (PSBs). Figuring among the top 20 banks with the highest gross non-performing asset (GNPA) ratios, according to CARE Ratings’ analysis of the first quarter results of 38 banks, PSBs are more stressed than their private sector counterparts. On a quarter-on-quarter basis, the increase in NPAs has been the highest in Quarter 1 FY18 witnessing a rise of 16.6 per cent, achieving `8,293,380 million as of June 2017. This study is an effort to study the impact of NPAs, causes, suggestive measures and the need of recapitalisation of PSBs to tackle the crisis. It further suggests a standardised model which can help banks to keep in check of additional capital required for maintaining minimum CET 1 as per regulatory norms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".