An investigation into the socioeconomic factors correlated with PISA reading scores
Bibliographic record
Abstract
Literacy is a fundamental skill that is essential to navigate daily life in modern society. Its importance is emphasized by Target 4.6 of the United Nations Sustainable Development Goals, which aims to “ensure that all youth and a substantial proportion of adults, both men and women, achieve literacy and numeracy” by 2030. This study utilized indicators of the Human Development Index to determine the socioeconomic factors most correlated to a country’s youth literacy rates, as measured by its score on the literacy section of the Programme for International Assessment (PISA). We examined the relationship between national PISA reading test scores and nine socioeconomic factors: a country’s mean years of schooling, primary school enrollment, secondary school enrollment, gross domestic product (GDP), GDP per capita, government spending on education, life expectancy, infant mortality, and total population. Triennial data spanning from 2000 to 2018 was gathered for over 80 countries and analyzed by year using simple linear regression models. A combination of Google Sheets, Microsoft Excel, and Python libraries was used to analyze the relationship between literacy and our chosen socioeconomic factors to determine which of the factors were most strongly correlated with youth literacy rates. Our results were interpreted using R2 and Pearson correlation coefficient values. From these results, we concluded that infant mortality, mean years of schooling, and life expectancy exhibit the strongest correlations with PISA test scores, suggesting that the strength of a country’s healthcare system is highly orrelated with its quality of education.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".