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Estimación de la previsibilidad de las revistas de acceso abierto cuestionables
Han Zhuang1, Lizhen Liang2, Daniel E Acuna3
1Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China.
Science advances
|August 27, 2025
Resumen
La inteligencia artificial (IA) puede identificar sistemáticamente las revistas cuestionables mediante el análisis de datos de sitios web. Este enfoque escalable ayuda a la integridad de la investigación al marcar miles de publicaciones sospechosas, complementando la revisión de expertos.
Área de la Ciencia:
- Información bibliométrica
- Comunicación académica
- Integridad de la investigación
Sus antecedentes:
- Las revistas cuestionables representan una amenaza significativa para la integridad de la investigación global.
- El control manual de las revistas a menudo es lento y carece de escalabilidad.
- La identificación de lugares de publicación académicos depredadores o poco confiables es una preocupación creciente.
Objetivo del estudio:
- Explorar el potencial de la inteligencia artificial (IA) para identificar sistemáticamente las revistas cuestionables.
- Desarrollar y evaluar un método basado en la IA para evaluar la legitimidad de las revistas.
- Proporcionar una solución escalable para detectar lugares de publicación académicos no confiables.
Principales métodos:
- Utilizó inteligencia artificial (IA) para analizar el diseño del sitio web de la revista, el contenido y los metadatos de la publicación.
- Entrenó y evaluó el modelo de IA contra extensos conjuntos de datos anotados por humanos.
- Los umbrales de decisión ajustados para equilibrar el cribado exhaustivo con la identificación precisa.
Principales resultados:
- El método de IA logró una precisión práctica en la identificación de revistas cuestionables.
- Más de 1000 diarios sospechosos fueron marcados en un umbral equilibrado.
- Estas revistas marcadas publican un número sustancial de artículos y reciben citas significativas.
Conclusiones:
- La IA ofrece una herramienta potente y escalable para mejorar los controles de integridad de la investigación.
- La clasificación automatizada utilizando IA puede identificar con eficacia las revistas potencialmente problemáticas.
- La integración de la IA con la revisión de expertos es crucial para la verificación sólida de los lugares académicos.
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