Financial Literacy: A Case Study for Portugal
Bibliographic record
Abstract
This work aims at understanding the level of financial literacy in Portugal, identifying the determinants of financial literacy in the Portuguese population, taking as an example certain sociodemographic factors such as gender and age. The aim is to understand whether there is a high level of adherence to financial literacy programs and initiatives, as well as the impact of financial knowledge variables on the financial literacy of the Portuguese population. The methodology used was quantitative and based on a questionnaire survey. The sample consisted of 600 individuals, all over 18 years old. It was concluded that individuals in the 26 to 35 age group had the best knowledge and that this sample showed better knowledge of interest rates compared to inflation and risk. The exploratory factor analysis shows five factors that determine the financial literacy of the Portuguese population and the way they manage their finances, which are (1) the perception of their current financial situation; (2) planning and controlling personal finances; (3) the perception of risky financial assets; (4) the perception of risk-free financial assets; and (5) savings. This research contributes to expanding scientific understanding in the field of financial literacy and offering support to the review of financial education policies by formulators, aiming to develop tools that help improve the financial behavior of the Portuguese population.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".