(untitled) (gjstudies, 2019)
This research investigates the relationship between public sector financial reporting transparency and accountability outcomes through a novel computationalframework that integrates natural language processing, network analysis, and evolutionary algorithms. Unlike traditional accounting studies that rely on manualcontent analysis or survey-based metrics, we develop an automated system capable of quantifying transparency across unstructured financial documents from142 municipal governments over a ten-year period. Our methodology introducesa multi-dimensional transparency index derived from semantic analysis of narrative disclosures, structural analysis of financial statement linkages, and temporalanalysis of reporting consistency. We employ a bio-inspired optimization algorithm—specifically an artificial immune system model—to identify optimal transparency configurations that maximize accountability outcomes measured throughcitizen engagement metrics, audit opinion improvements, and fiscal performanceindicators. The findings reveal non-linear relationships between transparency dimensions and accountability, with diminishing returns beyond certain thresholdsand unexpected interactions between narrative clarity and data granularity. Weidentify three distinct transparency archetypes among municipalities and demonstrate how evolutionary pressure from public scrutiny shapes reporting practicesover time. The study contributes to both public administration and computationalsocial science by providing a dynamic, systems-oriented approach to understandingfinancial governance that moves beyond static compliance frameworks. Our computational model offers public sector entities a diagnostic tool for evaluating andoptimizing their transparency strategies to enhance democratic accountability.