Investors Perceptions about Kisan Vikas Patra (KVP): Evidence from Agartala
Bibliographic record
Abstract
The objectives of the study is to report the motivating factors of respondents for investing in Kisan Vikas Patra (hereafter, KVP), a post office saving scheme and to assess the perceptions about the strategies to make it as an efficient investment instrument. An interview schedule comprising 41 questions with personal interview has been used to collect data from a sample size of 153 respondents chosen using different non-probability sampling techniques. A protocol interview with 10 respondents followed by a pilot survey with 20 respondents is carried out to check the validity of the questions, reliability and sample adequacy test is also performed. Through Factor analysis, five major factors are extracted. Based on such factors, students t-test is carried out. The findings indicate that a number of factors ranging from safety, liquidity, investment and maturity in cash, satisficing return, non-disclosure of sources of income and Permanent Account Number (PAN) motivate for invest in KVP. It documents that a number of issues need to be amended like allowing tax benefits, reduction in maturity tenure, rationalizing KYC norms, increase in the rate of return, provision for e-certificate and e-investment etc. The study also acknowledges its few limitations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".