Education, Science and Technology in Mexico: Challenges for Innovation
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".