The Colombia–Ecuador Border Region: Between Informal Dynamics and Illegal Practices
Bibliographic record
Abstract
This paper examines two different phenomena in the Amazonian border region, taking into account the analytical framework of international relations to understand the way in which States face them: (1) the informal dynamics of everyday life that have been part of the State formation process, and (2) illegal practices. We argue that they are different but not independent processes. First, informal sectors are part of the “political economy of war,” due to the incipient consolidation of the State, linked to historical isolation and the strong influence of the internal armed conflict in Colombia’s border region and its transnational dynamics in Ecuador. Legal activities often finance illegal activities of non-state armed actors and depend on it. Second, public policies in the region are based on a national or regional security point of view without articulating with regional integration policies. Finally, States act individually in the Andean region with no policies of cooperation at all. This lack of articulation has had a negative impact on human security. States’ responses to illegal activities have failed, leading to a capturing of the political system. Nonetheless, guerrilla and Colombian government peace talks have opened a new path to think differently on how to consolidate the State control and to build social linkages based on regional integration, social inclusion and consolidation of democratic rule.
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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 | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".