Digital payments in India: An evaluation of performance for the year 2020- 21
Bibliographic record
Abstract
Digital payments have shown a tremendous rise post demonetization in 2016. However, they suffered some setback in the year 2020-21 that was hit by the Covid-19 pandemic. The first quarter of the financial year 2020-21 particularly showed a sizable dip in comparison with the same period for the financial year 2019-20. However, with economic activity returning back to some normalcy from second quarter onwards, both the volume and value of digital payments picked-up pace and recorded positive growth in the year 2020-21 in comparison with the same period for the financial year 2019-20. This article carries a review of the performance of the digital payments in India for the year 2020-21 with a comparison with financial years 2018-19 and 2019-20. The article also evaluates the actual achievements of the RBI for the year 2020-21 in the area of digital payments as against the goals set.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".