The influence of financial attitude, behaviour and knowledge on financial literacy: a Malaysian perspective / Amira Rosalia Aripin
Bibliographic record
Abstract
Financial Literacy among youths is a global concern worldwide. This includes alarming problems such as the high bankruptcy level among youths and the weaknesses in managing finance among youths such as managing debt, personal finance and savings. The GECD has provided a framework which assess the Financial Attitude, Financial Behaviour and Financial Knowledge to determine the level of Financial Literacy among youths. However, the result will very much vary within countries such as first world countries like UK, Canada, and Germany and third world countries like India, Bangladesh and Vietnam. Thus it is significant to carry out the study in Malaysia. This study has been conducted among 220 youths with purposive sampling method. It uses primary data which is derived from questionnaires that have been distributed to individuals from different field of expertise and working fields. Secondary data are also used to help researcher in getting a clearer picture of the situation on a global perspective. The definition of youths in the Malaysian context refers to those 30 years old and below. The time frame of conducting the study is within the year 2019 and it is not time significant. The result has shown that between the variables 'Financial Knowledge' is the most significant in determining the level of financial literacy among youths in Malaysia. However, there are some limitations in the study such as the sample size, the scope of variable used and medium to collect the data.
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.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".