Bibliographic record
Abstract
Intro duct ionIt is not an exaggeration to say that extensive development of the private sector is one of the most the most remarkable phenomena in the recent evolution of higher education in Mexico, if not its most notable characteristic.This flood, particularly that of local and international online 'diploma mills', has provoked another kind of torrent: a surge of alarming media statements by rectors and presidents of public and private universities warning against the deluge of mediocrity and fraud that threaten to overcome higher education and perhaps undo the fruits on fifteen of quality improvement policy.Policymakers have also taken part in this debate; responding with tighter licensing procedures and invocations to deepen and extend accreditation, which is still in its infancy.There is an emerging academic literature on the subject (Silas Casillas 2005;Rodríguez 2003; Villa Lever 2003) to which this research intends to contribute.This chapter 1 explores the recent growth of private higher education in Mexico, specifically underscoring the new patterns in institutional and regional diversification and presenting a tentative institutional typology 1 The research reported here was done through the Alliance for International Higher Education Policy Studies project, a collaborative effort focused on understanding the relationships between policy and performance in the higher education systems of Canada, the United States, and Mexico, with support from the Ford Foundation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".