The Influence of Gender and Ethnicity on Young Adults’ Participation in Financial Education Programme
Bibliographic record
Abstract
A major problem encountered by educationalists, community leaders and policy makers is to transfer financial literacy and consumer education successfully to their community. Delivering of financial education for youth of a country is one possible intervention to improve the financial capabilities of a population. Therefore, for an effective training we have to identify their financial needs. Further they need guidance and access for financial knowledge and money management tools. Therefore, the objectives of this study are to identify the training needs of youth by gender and ethnicity about money management and to determine their interest towards it. The sample was comprised of 220 secondary school students from five schools in Greater Klang Valley/Kuala Lumpur with 112 females and 108 males and the data collection was done using self-administered questionnaire. The results shown that about one third of female youths have preferred to participate on financial literacy programs than male youths (21.5%). In addition, most of the Malay respondents said that they need more information to take efficient decisions on saving, borrowing and insurance, followed by Indians (64%) and Chinese (61.5%). The findings of this study would be used to the development of financial empowerment program of youth in Malaysia in order to enhance their financial literacy.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".