Using Plastic Money (Cards) in Kathmandu Valley: Users’ Knowledge, Current Use, Challenges and Way-forward
Bibliographic record
Abstract
This study aims to understand the users’ knowledge about plastic money, its current use, challenges they faced and way-forward. Based on descriptive research design, primary data is used for the purpose as per its suitability. A structured questionnaire has been arranged with the help of KOBO and devised for the information assortments from 404 plastic money users. Results found that people who use plastic money usually work in banks and financial institutions (33.87%) and are from the nuclear family (62.62%), with income between 25001 – 50000 (56%). 95.79 % of the respondents know about plastic money, and 86.3 % have plastic money. 88 % of respondents said they feel safe while using plastic money. 40.72% of respondents have faced challenges and problems while using plastic money. The majority (79.28%) of respondents believed that using the bank's services could be solved. It can be solved by giving training (34.85%), quick response to the problem raised by users (44.32%), update technology (34.47%) and keep a good network in the ATMs (71.79%) and quick solutions to the user's problems (75.76%). This study concludes that hassle-free transactions, a low-interest rate of credit cards, attractive advertisement, and awareness of how plastic money can use help and attract users of plastic money. .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".