Colombia’s long road toward peace: implications for environmental human rights defenders
Bibliographic record
Abstract
Human rights defenders, social leaders, and environmental and indigenous activists fight for political, cultural, social, economic, and environmental rights and often face intimidation and violence as a consequence. In this article, we analyze how the implementation of the peace agreement signed in 2016 between the government of Colombia and the FARC-EP guerrilla group affects environment and human rights defenders (EHRD) in Colombia. Despite the expectation of a more peaceful future following the peace agreement, EHRD have faced increased intimidation and violence in Colombia, making it the most dangerous country for EHRD globally. We seek to understand this counter-intuitive development through Fraser’s theory of social justice that stresses the need for integrated measures to address economic, political, and cultural injustices in parallel. The theory argues that a focus on correcting cultural misrecognition and political underrepresentation of vulnerable groups may, paradoxically, mask or even facilitate further injustices, if that focus is not matched by sufficient efforts to address economic maldistribution. The fate of EHRD since the peace agreement reflects such an imbalance in justice priorities, which is in the way of lasting and sustainable peace. Drawing on data from secondary sources, ethnographic interviews, and an analysis of policies and laws, we find that, since the peace agreement was signed, new forms of maldistribution have emerged and solidified in the country, including land grabbing, displacement of local populations, resource extraction, and illicit economies, which are strongly related to the growing influx of drug cartels. Despite the increasing advocacy of international organizations and regional legal agreements to protect EHRD, they are caught in precarious roles between cultural recognition and political and economic abandonment by state institutions and are affected by the global trade in illicit products, and the demand for land for agricultural products and minerals. This finding, we argue, warrants more research into the imbalance of addressing local and global injustices during peace processes and its fatal implications for EHRD in Colombia and globally.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".