Impacto de la corrupción en la economía: una propuesta de análisis, desde la teoría de redes
Bibliographic record
Abstract
Se presenta un análisis con enfoque empírico para modelar la corrupción por medio de herramientas de la ciencia de la complejidad, y en concreto, la ciencia de redes complejas, con un caso que tiene lugar en México durante el período 2010 a 2016 que involucra una red de empresas con sede en el estado de Hidalgo, utilizadas para malversar recursos públicos. El objetivo de esta investigación es realizar un análisis de la naturaleza sistémica de los eventos de corrupción ocurridos en México a través de empresas y funcionarios que hacen uso del recurso público para fines personales, mostrando de esa manera las deficiencias de los análisis reduccionistas. Se describe la estructura y dinámica de esta red a través de un modelo de un mundo pequeño, utilizando la información disponible relacionada con dichas empresas, se pudo medir parámetros como la densidad, el diámetro, la longitud promedio de la ruta, el grado promedio, modularidad, coeficiente medio de clustering y centralidad de vector propio, para proporcionar evidencia metódica sobre las características que pueden indicar corrupción y el impacto económico que generan.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| 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".