Financial Literacy Education for Women: A Novel Approach based on Social Media Platform
Bibliographic record
Abstract
According to the latest data, the financial literacy index in Indonesia stood at 38%. This figure was definitely an improvement compared to previous years, but was still considerably behind compared to the global standard. Taking into account gender disparity in Indonesia, this figure becomes even more abysmal as the financial literacy index for women is substantially lower than men. To solve this problem, a social media knowledge management framework is proposed to structure and disseminate financial knowledge with the purpose of improving the financial literacy index, especially for women, in Indonesia. This knowledge management platform is supported by a financial education program that has been developed based on the characteristics and needs of typical women in Indonesia. To further substantiate the findings of this study, a series of in-depth interviews were conducted with the aim to acquire a better understanding of various aspects of financial literacy from the perspective of women. In total, 13 women who lived in Bandung were chosen as the main subject of this study based on a judgment sampling technique. Encouragingly, more than three-quarter of the respondents were considered to be well-literate. Digging deeper into the data, however, presents a much more nuanced and complicated insight regarding the behavior of the respondents as most of the respondents simultaneously exhibit a very wide range behavior ranging from discipline to carelessness when managing their 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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".