Inequalities in Mexican Children’s Schooling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article we measure the evolution of inequalities in basic school attendance and educational attainment of Mexican children from 1960 to 2000. During this time, the Mexican government made great efforts to extend education to the most disadvantaged sectors of society, particularly rural communities. Nevertheless, differences in educational attainment persist. We focus on three types of inequality in education: urban/rural, gender, and ethnic inequality as indicated by speaking an indigenous language. The structure and economic resources of families also mediate the amount of education that children receive. To this end, we examine the determinants of primary completion and secondary school enrollment in rural communities in ten of the poorest states in Mexico, paying particular attention to differences between boys and girls and indigenous language and Spanish-language speakers.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it