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Record W2784022015 · doi:10.18687/laccei2016.1.1.400

The Impact of International Quality Assurance on Mexican Higher Education

2016· article· en· W2784022015 on OpenAlexaboutno aff
Enrique Atanacio Morales González, José Humberto Loría Arcila, Miriam del C. Olaldez, Margarita Díaz Flores Castillo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Teacher Training
Canadian institutionsnot available
Fundersnot available
KeywordsQuality assuranceQuality (philosophy)Computer scienceBusinessMarketingPhysics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.002
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.114
GPT teacher head0.492
Teacher spread0.378 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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