Education, Science and Technology in Mexico: Challenges for Innovation
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
The innovation process is founded on a high-quality education system at all levels, which trains scientists and technologists capable of generating innovations. Education is the most decisive factor in human development, yet in Mexico current statistics reveal a critical situation at every educational level, as only 1 out of every 10 children entering elementary school obtains a university degree, and less than 0.01% of the population holds a doctoral degree. In addition, international tests such as the Programme for International Student Assessment (PISA) reflect the low educational performance of Mexican students in several subject areas. The deficiencies found in the national education system negatively impact innovation indicators. Although there have been major initiatives to reverse underperformance in education, science, technology and innovation (STI), the country has actually seen its global competitiveness ranking fall from 55th in 2013 to 57th in 2015, and structural reforms in education, science and technology proposed since 2012 have still not been successfully implemented. This paper analyses the current status of the education and STI systems in Mexico and sets out some strategies to improve public policies to profit from the great competitive advantages that Mexico has as an emerging economy, with about 52 million economically active people and great untapped potential if innovations policies are implemented successfully.
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.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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