Absent, Repressive, and Criminalized States: Forced Internal Displacement and Irregular Migration in El Salvador, Honduras, and Guatemala
Bibliographic record
Abstract
Nearly a million people from El Salvador, Honduras, and Guatemala have been internally displaced in recent years, and hundreds of thousands more have fled to Mexico, the United States, Canada, and Europe. An extensive literature provides evidence that direct and structural violence are the principal drivers of this phenomenon. Largely unexamined is the way in which the governments of the region contribute to and in certain critical respects create the conditions that underlie this mass human movement. Exploration of this gang-related forced displacement and irregular migration reveals the role of absent, repressive, and criminalized state postures and the corresponding neglect of the lower-income sectors as contributors to the crisis. Casi un millón de habitantes en El Salvador, Honduras y Guatemala han sido desplazados internamente en los últimos años, y cientos de miles más han huido a México, Estados Unidos, Canadá y Europa. Una extensa literatura muestra que la violencia directa y estructural han sido los principales impulsores de este fenómeno. Sin embargo, no se ha examinado la forma en que los gobiernos de la región contribuyen y, en ciertos aspectos críticos, generan las condiciones que subyacen este masivo movimiento humano. Una exploración de dicho desplazamiento forzado y migración irregular ligados a la violencia pandillera nos revela el papel que cumplen las posturas estatales ausentes, represivas y criminalizadas, así como el correspondiente abandono de los sectores de bajos ingresos como contribuyentes a la crisis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".