Balancing Progress and Democracy: Mexico’s Governance Under Sheinbaum
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
With the election of Claudia Sheinbaum as Mexico’s first female president on June 2, 2024, the country entered another six-year term under the leadership of the left-wing Morena Party. The previous president, Andrés Manuel López Obrador (AMLO), also of Morena, ruled from 2018 to 2024. This remarkable electoral outcome raises important questions about longer-term trends in Mexican governance and invites comparisons with neighboring countries. Using insights from the 2024 Berggruen Governance Index (BGI), we can look deeper at Mexico’s trajectory. We find that, since 2000, Mexico has struggled to catch up with more developed nations like the U.S or Canada in the governance measures but still outpaces many other Latin American countries. The Morena administrations have embarked on ambitious programs of state building and economic nationalism, but these efforts have been criticized for contributing to democratic backsliding, particularly in the conservative U.S. press. Still, Mexico has achieved a solid degree of economic success, rebounding rapidly after the pandemic and GDP per capita increasing by over 50% since AMLO took over in 2018. Much of this growth has been driven by traditionally poorer southern regions in the country. Despite some signs of success, one of Sheinbaum’s key dilemmas will be to balance a more expanded and effective reach of the state—both geographically and economically—without undermining democratic norms. Given resistance to changes like judicial reform, resource nationalization, and use of the military for state-building projects, this will be a difficult balance to strike.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.071 | 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