Paramilitary Conflict in Colombia: A Case Study of Economic Causes of Conflict Recidivism
Bibliographic record
Abstract
Following the peace accord on 26 September 2016 between the Colombian government and the Colombian Revolutionary Armed Forces (FARC), significant structural issues persisted in Colombia, such as state fragility, land distribution challenges, and rural impoverishment, all of which jeopardized sustainable peace. Previous disarmament events indicated potential shifts in violence and recidivism rates among ex-combatants. This paper aims to determine the likelihood that, in the post-conflict era with FARC, these ex-combatants would rearm themselves into new criminal factions. Employing a methodology by Paul Collier, the study utilized logit, probit, and panel data models with both fixed and random effects to evaluate the recidivism risk at the municipal level. A 1% increase in per capita municipal income decreased conflict probability due to the increased opportunity cost of disrupting economic endeavors. Conversely, 1% increases in potential conflict benefits from tax revenue and natural resource proceeds raised the probability of conflict by 40% and 17%, respectively. Key results indicate that economic advancement, as measured by per capita income, reduced the duration of paramilitary presence, whereas revenue from taxes and natural resources extended it at the municipal level in Colombia.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".