The factors influencing financial success: A Machine Learning approach
Bibliographic record
Abstract
This paper explored the various socioeconomic factors that contribute to individual financial success using machine learning algorithms and approaches. Financial success, a critical aspect of an individual’s well-being, is a complex concept influenced by various factors. The objective of this study was to understand the determinants of financial success. The National Longitudinal Survey of Youth 1997, by the Bureau of Labor Statistics was examined, consisting of longitudinal data of a sample of 8984 individuals. The dataset comprised income variables and a large set of socio economic variables. The findings highlighted the significant influence of the highest education degree obtained, occupation and gender as the top three determinants of individual income among the socio economic factors examined. Yearly working hours, age and work tenure followed as the three secondary influencing factors. All the other factors including parental household income, industry, parents’ highest education grade and others were identified as tertiary factors. These insights will allow researchers to better understand the complex nature of financial success, and advance broader societal well-being by providing insights for policymakers during decision-making processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".