Performative infrastructures: Populism and the material politics of militarization in contemporary Mexico
Bibliographic record
Abstract
Upon taking office in December 2018, Mexican President Andrés Manuel López Obrador (AMLO) began financing large-scale infrastructural projects across the country, including the Felipe Ángeles International Airport (AIFA), to be built and managed by the Mexican Armed Forces. Over 2 years into its 2022 inauguration, the AIFA has negligible air traffic but an enormous presence in the public sphere. Drawing on literature on populism and the politics of infrastructure, this article explores how the airport’s main role lies less in its logistical operations than in redrawing the relationship between the Mexican Army and “the people.” Through ethnographic and media analysis of the airport’s abundant propaganda—particularly a feature-length documentary—we analyze how this infrastructure serves as a site for ideological work by and for the Army. We argue that, by helping to normalize militarization as they advance it by their construction and operation, infrastructures may possess the performative power to rewrite the boundaries between civilian and military life. By mobilizing the tools of advertisement and propaganda, infrastructures may showcase processes like Mexico’s militarization in sanitized and partial ways. This article thus situates infrastructures not as the product of a political order, but rather as capable of bringing a new such order into existence.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".