Using Plastic Money (Cards) in Kathmandu Valley: Users’ Knowledge, Current Use, Challenges and Way-forward
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it