Balancing Progress and Democracy: Mexico’s Governance Under Sheinbaum
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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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".