The visual economy of migration and the production of crisis. Two cases in question: Norte de Santander and the Darien
Bibliographic record
Abstract
This paper examines the role of visual representations in shaping public perceptions and policies regarding Venezuelan and transcontinental migration in Colombia, with a focus on two key border areas: Norte de Santander (the Colombia-Venezuela border) and the so-called “Darién Gap” (the Colombia-Panama border). By analyzing images and narratives published by El Espectador between 2019 and 2024, the research examines how bodies and places are visually represented to shape public perception, policy responses, and migration governance. The analysis reveals two dominant themes in the media’s portrayal of migration: (1) The production of migrant bodies – Migrants are frequently depicted as massified, vulnerable, and dehumanized subjects. Venezuelan women, in particular, are portrayed through a dual lens: as victims of sexual violence and as reproductive subjects, reinforcing narratives of crisis. The bodies of migrants in the Darién are often visually merged with the landscape, emphasizing suffering, exhaustion, and precarity. (2) The production of spaces – The border regions are framed as zones of disorder and danger. The trochas (irregular crossings) in Norte de Santander are depicted as lawless, reinforcing associations with crime, smuggling, and state absence. Meanwhile, the Darién Gap is visualized as a treacherous jungle where nature itself becomes an obstacle, justifying increased border control and humanitarian interventions. By applying multimodal discourse analysis, the study demonstrates how visual representations produce emotions— such as fear, pity, or urgency—that influence both public opinion and policy decisions. For this reason, we propose the concept of “visual economy of migration” to understand how visual representations shape the shared meaning of the migrant crisis in Colombia, while entangling readers’ fears and concerns with broader issues.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".