Analysis of the knowledge of young people in the São Paulo metropolitan region about pension plans
Bibliographic record
Abstract
According to the Brazilian Institute of Geography and Statistics (IBGE), the number of people aged 60 years or older in Brazil exceeded 31.23 million in 2021. Projections suggest that this number may exceed 58 million by 2060, which means that the elderly population may represent more than 25.5% of Brazilian society (IBGE, 2022). Thus, this study aims to investigate the knowledge of young residents of the metropolitan region of São Paulo on Social Security and Private, as well as to analyze whether they are preparing financially for the future. Bibliographical research was carried out conceptualizing the two types of social security existing in Brazil, as well as conducting the field research addressing the main topics. The methodology was carried out through the preparation and application of a questionnaire composed of 12 questions related to the theme, directed to an audience of the metropolitan region of São Paulo aged between 18 and 25 years, the research was carried out on-line between the 13th and 20th of April. Through the questionnaire, we obtained 150 answers and 136 considered valid for being part of the established sample. Thus, it was noted that most respondents do not have knowledge about the subject related to social security, as well as do not have planning for retirement or investments in other roles, significant part justified by not having enough knowledge to invest. It was possible to prove these arguments from the answers obtained.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".