A RESEARCH ON THE ANALYSIS OF MERGER OF SBI WITH ITS 5 ASSOCIATE BANKS AND BHARTIYA MAHILA BANK
Bibliographic record
Abstract
The most recent and largest merger in the history of banking industry took place on 1 April, 2017. State Bank of India merged with its 5 associate banks namely State Bank of Bikaner and Jaipur (SBBJ), State Bank of Hyderabad (SBH), State Bank of Mysore (SBM), State Bank of Patiala(SBP), State Bank of Travancore(SBT) and Bharatiya Mahila Bank. Shares of State Bank of India (SBI) and its listed associate banks (State Bank of Bikaner, State Bank of Mysore and State Bank of Travancore) gained 3-13 percent on the back of approval from the cabinet for their merger. The merger will bring nearly a quarter of all outstanding loans in India’s banking sector to SBI’s books. With this step SBI has entered into the list of top 50 global banks. However, there were many imponderables involved in this big merger, like, employees issues related with redeployment or loss of jobs, transfers, new working conditions, increased working hours, etc.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".