“Migration and Violence: Lessons from Colombia for the Americas” A workshop of the Transatlantic Forum on Migration and Integration and the Refugee Research Network (TFMI)
Bibliographic record
Abstract
The conference, “Migration and Violence: Lessons from Colombia to the Americas” was held in Bogotá D.C., Colombia at the Pontifi cia Universidad Javeriana on June 29, 2012. The main objective of the conference was to develop inter-disciplinary academic research in Central America and Mexico regarding the relationship between violence, particularly narco-violence, and migration. The setting in Bogotá D.C. was deliberate as the participants discussed how lessons learned from Colombia’s experience with narco-induced migration could be leveraged for the benefit of Central America and Mexico. With the participation of experts on international migration, government representatives, academics, and civil society, the conference highlighted research results and relevant intervention experience concerning this problem in Colombia, El Salvador, Guatemala, and Mexico.This article presents an analysis of the presentations given and the discussions held at the conference. It consists of four parts. The first section compares the similarities and differences regarding migration and violence in Colombia and El Salvador, Guatemala, and Mexico. The second section presents the major epistemological challenges emerging from research and models of intervention. The third section presents the implications of the epistemological challenges and their impact on public policy. The fourth section concludes with principal lessons from Colombia for research and intervention in the problem of violence and migration.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".