Convergences and Divergences in the 2024 Judicial Reform in Mexico: A Neural Network Analysis of Transparency, Judicial Autonomy, and Public Acceptance

The paper operates within the established paradigm of computational social science, specifically applying artificial intelligence and neural network modeling to the field of public policy analysis and legal studies. It uses quantitative simulation to evaluate the potential impacts of legislative changes, a common practice within this research framework.

The classification is Normal Science because the research applies an existing methodology—neural network analysis—to solve a specific "puzzle": predicting the viability and public acceptance of Mexico's 2024 judicial reform. The paper does not propose a new scientific theory or challenge the foundational principles of using computational models for policy evaluation. Instead, it extends the scope of the current paradigm by demonstrating its application to a new, complex socio-legal problem. This work is a "mopping-up" operation, refining the application of established tools to increase understanding within the existing scientific framework.