Seguridad pública y criminalidad: el caso del departamento de Sucre en 2014
Bibliographic record
Abstract
Este estudio tiene como objetivos generales: 1) analizar la dinamica de la criminalidad en el departamento de Sucre (Colombia) en el ano 2014, de cara a la inseguridad en la sociedad global y a la luz de la teoria de la seguridad publica; e 2) identificar el delito mas frecuente, su ubicacion a nivel municipal y microlocal y la poblacion afectada. Utilizando la metodologia del estudio de caso se analiza, de manera comparativa y multidisciplinar, el comportamiento de la actividad delincuencial en este departamento. Los resultados del estudio indican que, en materia de criminalidad, el homicidio es el delito de mayor frecuencia. Sin embargo, la perspectiva comparativa nos permitio establecer que Sucre ha registrado historicamente tasas relativamente bajas de homicidios con relacion a la media nacional y a otros departamentos. En 2014, mientras Sincelejo, la capital departamental, redujo su tasa de homicidios, los municipios de San Onofre y San Marcos registraron una tasa de homicidios superior a la media departamental. En conclusion, en materia de inseguridad, Sucre ha seguido la tendencia del pais. Su tasa de homicidios aumento entre los anos 1980 y 2000 y ha disminuido desde 2005. El sicariato fue la principal causa de muertes violentas en 2014.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".