The Border, Performed in Films: Produced in both Mexico and the US to “Bring Out the Worst in a Country”
Bibliographic record
Abstract
Border scholars have long understood borders as social constructions around territories and identities. Drawing on interdisciplinary perspectives, my objective is to analyze the cultural production and “othering” processes of the multiple US–Mexico borderlands via good-quality films emanating from both Mexico City and the US, particularly Hollywood, in two periods: historical background on the 1930s–1980s and the contemporary period of the last two decades. I compare differences across multiple border sites along the near 2,000 mile line—west coast Pacific, central El Paso-Ciudad Juárez, and east coast Gulf of Mexico—as well as those sites inbetween. My overarching argument is that the film industry itself brings out the worst of countries in the US–Mexico borderlands. By “worst,” I mean lawlessness, sexual violence, deaths, and drugs, with “othering” processes alive and well on both sides of the border. As such, in both historical and contemporary films, everyday lives in the borderlands are not well represented.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".