The Impact of International Quality Assurance on Mexican Higher Education
Bibliographic record
Abstract
This document describes the international trends in educational field and its impact on the quality of Higher Education for engineering in Mexico. Due to the expansion of student enrollment in higher education, a wide education availability, new technologies and emerging global networks, higher education has been involved in a constant change of methods to demonstrate quality on national and international level. In this paper, the term "quality" is defined as a comparison reference between several homologous elements or under a certain reference standard. Besides, the concept of Quality Assurance is presented beneath two scopes: responsibility and improvement; The first one is focused on the accountability that Higher Education Institutions must do to the State and Society; And the second one is focused in the internal control and continuous improvement of the effectiveness of education.In other words; there are two principal perspectives: an external approach and an internal approach.This last view is taken by most countries in America and Europe, promoting the creation of bodies responsible for accrediting engineering programs in international level.In Mexico, there are more than 70 higher education engineering programs accredited by at least an internationally recognized accreditation association.Considering the principal accreditation associations on Canada, United States, Mexico, Europe and Latin America, a comparison is made of the main criteria and standards with which they evaluate each of the different organisms to engineering programs in order to identify similarities.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".