Unveiling The Journey: Analysing The Atal Pension Yojana's Performance
Bibliographic record
Abstract
NSSO's 2011-12 Survey Reveals Stark Reality: 88% of India's Workforce Lacks Social Security Coverage. In response to this alarming statistic, the Government of India launched the Atal Pension Yojana on May 9, 2015, with a specific focus on safeguarding the elderly, particularly those in the unorganized sector. Designed to ensure a reliable income stream post-retirement, especially for those aged 60 and above, the scheme aims to bridge the gap in social security coverage. Financial institutions were entrusted with the task of enrolling individuals into the scheme, prompting a need to assess their performance. Through secondary data collection from bank and government records, trends in subscription were analysed using Excel. Analysis revealed that among the six financial institutions studied, Public Sector Banks emerged as frontrunners in subscriber enrolment under APY since its inception. This success can be attributed to the collaborative efforts of the Government of India and these financial entities in reaching out to potential subscribers. In just six years, the scheme has garnered substantial traction, with approximately 3.30 crore individuals enrolled as of August 25, 2021, signalling its significance in addressing the retirement concerns of India's workforce.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".