The Historical and Contemporary Causes of «Survival Migration». From Central America’s Northern Triangle
Bibliographic record
Abstract
Durante la última media década se han producido salidas masivas de refugiados procedentes del Triángulo Norte de América Central, es decir, de El Salvador, Guatemala y Honduras. Dichas salidas surgen de una serie de procesos históricos perversos, que se refuerzan mutuamente: la implementación profundamente defectuosa de acuerdos de paz igualmente defectuosos que puso fin a las guerras civiles de la región en la década de 1990; la búsqueda de la privatización neoliberal y las políticas económicas «amigables al mercado» que socavan el avance hacia una paz social sostenible; los acuerdos comerciales que infligieron un gran daño a la agricultura campesina; la búsqueda de inversión extranjera en sectores extractivos que desplazaron a pueblos rurales e indígenas y las políticas de las principales instituciones internacionales y del gobierno de Estados Unidos en particular, que profundizaron todas estas tendencias perversas que dejaron a la gente sin medios de vida. Las bandas criminales y la violencia vinculadas al narcotráfico son manifestaciones de estos procesos subyacentes que expulsan a las personas de la región en oleadas de «migración de supervivencia» forzada. The past half decade of massive refugee outflows from the Northern Triangle of Central America –that is, from El Salvador, Guatemala, and Honduras– emerge from a number of perverse and mutually reinforcing historical processes: the deeply flawed implementation of equally flawed peace accords that ended the region’s civil wars in the 1990s; the pursuit of neoliberal privatization and «market-friendly» economic policies that undercut advance toward sustainable social peace, including trade agreements that inflicted great damage to peasant agricultura; the pursuit of foreign investment in extractive sectors that displaced rural and indigenous peoples, and the policies of the major international institutions, and of the United States government in particular, which deepened all of these perverse trends that left people without livelihoods. The gang and criminal violence linked to the narcotics trade are manifestations of these underlying processes that expel people from the region in waves of forced «survival 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".