Compensatory Policies Attending Equality and Inequality in Mexico Educational Practice among Vulnerable Groups in Higher Education
Bibliographic record
Abstract
This paper presents an estimate of the prevalence of social inequality in accessing higher education among vulnerable groups in Mexico. Estimates were determined from statistical data provided by governmental agencies on the level of poverty among the Mexican population. In Mexico, the conditions of poverty and vulnerability while trying to access better standards of living as well as educational inequality continue to grow at an alarming rate. The number of poor (extreme and moderate) and vulnerable people (according to income and social need) increased from 2008 through 2010 dramatically. The number of people in this situation went from 89.9 million to 90.8 million, which represents 80.64% of the total Mexican population. Only 19.36% of the population is not considered poor or vulnerable. The access to higher education is not distributed uniformly throughout the Mexican youth since they belong to different social and economic strata: the least developed regions carry the largest share. Consequently, educational opportunities are unequally distributed mainly across age and gender factors. A distribution imbalance is also found with regard to gender throughout the population observed and analyzed: indigenous females have a significantly higher risk of not having access to higher education than males.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".