The impact of formal education on literacy and numeracy skills in Chilean adults: a comparative analysis with Latin American counterparts
Bibliographic record
Abstract
Chile and other Latin American countries consistently demonstrate the lowest scores in the international surveys of foundational information-processing skills. This paper examines the effect of formal education on the literacy and numeracy performance of Chilean adults and compares this effect with that observed in Latin American countries with similar information-processing skills. Using data from the Programme for the International Assessment of Adult Competencies (PIAAC), we conducted a comparative analysis of literacy and numeracy skills in the Chilean population relative to Mexico, Peru, and Ecuador. Our analysis revealed that, regardless of the years of formal education, the populations of these countries do not achieve a level 3 in literacy and numeracy skills (on PIAAC’s five-level scale), which is considered the minimum requirement for effective participation in today’s technologically-driven economy and society. We also observed that Chileans at higher levels of formal education (with a bachelor’s or higher university degree) are on par with or exceed the literacy and numeracy skills of the best-performing Latin American country, Mexico. Less educated Chileans, however, lag behind education-matched groups of Mexicans and rank with the lowest-performing countries like Peru and Ecuador, in both skills. These findings highlight critical implications for educators and policy-makers in Latin America, particularly concerning educational system effectiveness in developing crucial competencies. The analysis shows the impact of past and ongoing reforms in the Chilean school system and underscores the importance of addressing skill development across all educational levels for personal and professional success in contemporary society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".