Investigating the Influence of Poverty on High School Graduation Rates: An Analysis of the Impact of Socioeconomic Inequities on Adolescents: Winners of the 2024-25 National High School Data Analysis and AI Challenge for Sustainable Development. The publication is sponsored by RBC Future Launch, Let’s Talk Science, and NRC.
Bibliographic record
Abstract
As the fourth of the United Nations’ Sustainable Development Goals, education is a cornerstone of how our society functions. It equips individuals with essential skills and knowledge necessary for personal growth and informed civic contributions. Access to quality education enables people to break cycles of disadvantage, fosters innovation, and strengthens communities. However, socioeconomic disparities, particularly poverty, hinder the accessibility and quality of learning, especially for adolescents. Without intervention, these disparities perpetuate cycles of inequality, limiting academic opportunities and long-term success. Despite global efforts, educational inequities remain and need targeted solutions. This study investigates the effect of socioeconomic disparities on adolescents through analyzing the relationship between adolescent poverty and high school graduation rates across United States counties from 2021 to 2024. It leverages data science methodologies to analyze datasets from sources such as the United States Census Bureau. Unlike prior research, which examined these factors in isolation, this study discusses direct correlations between poverty rates with educational outcomes while proposing viable policy changes to ease inequalities. Key findings reveal a significant negative association between regional poverty levels and high school graduation rates. The correlation is further investigated and confirmed through comparing median salaries between individuals with and without high school diplomas, then between high school dropout rates and school government funding, all in the United States. Finally, this study offers evidence-based recommendations as well as future research directions to mitigate the impact of poverty on access to education, opportunities, and success for all adolescents.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".