Abstract

The reproducibility crisis in social sciences has revealed significant weaknesses in conventional research practices, including selective publication, questionable statistical methods, and opaque peer review processes. This paper introduces Granular Interaction Thinking Theory (GITT) as a novel framework for understanding the plausibility of scientific findings, conceptualizing knowledge validation as a structured entropy-reduction process. Within this framework, open science practices—such as open data, open review, and open dialogue—initially increase informational entropy by exposing inconsistencies. However, through iterative refinement, they ultimately enhance the robustness and plausibility of scientific knowledge. To systematically assess a study’s plausibility, we propose the Scientific Plausibility Index (SPI)—an entropy-based metric that integrates data and method transparency, replication success rates, and community engagement. Additionally, leveraging artificial intelligence (AI) and natural language processing (NLP), a dynamic plausibility-tracking system could be developed to detect unreliable claims early and accelerate scientific self-correction. This shift—from closed, one-time assessments to continuous, community-driven evaluation models—ensures that scientific knowledge remains rigorously tested and updated. We advocate for shifting from closed, one-time assessments to continuous, community-driven evaluation models, ensuring that scientific knowledge remains rigorously tested and updated. Ultimately, aligning incentives with entropy-reducing mechanisms—such as rewarding replication efforts and fostering open discourse—can cultivate a research culture that prioritizes robustness, transparency, and cumulative knowledge-building.