# Challenging Scientific Categorizations Through Dispute Learning
## 3 Results. Standardization of a Dispute Learning System
### Decidable Graph of Knowledge Design (DGKD): Building and Applications
#### 3.4.2 Results Representing the DGKD: Disputable Knowledge and Disputed Knowledge  
* **Data Types Compatible with GRAPHYP Analysis** GRAPHYP's methodology can analyze diverse types of scientific data:
* Research Consultation Patterns: comparison of methodological pathways; analysis of alternative approaches and results; laboratory and library usage patterns. * Bibliometric Analysis: Adversarial classification systems; contrasting conceptual frameworks; citation pattern analysis [94]. * Scientific Discourse Analysis. Argumentation mining represents the most promising direct application of GRAPHYP's methodology: analysis of scholarly debates and their evolution; tracking the development of competing theories [95]. * **Applications in Large-Scale Scientific Controversies** Real-world examples are numerous in scientific domains where scientists as well as laymen develop intense web usages of scientific results with disputed categorizations. Examples hereunder may give an overlook in general fields; the same is true in laboratory works or interactive participation in research discussions or congress. Approach to policymaking and education about climate change could be DL processed from the following uses:  
* The Intergovernmental Panel on Climate Change (IPCC), to enhance their assessment reports, could architecture an interactive way to represent the state of climate science knowledge. * The National Institutes of Health (NIH) could implement the GRAPHYP framework to provide a comprehensive, up-to-date resource for tracking disputes and evolving knowledge in COVID-19 research. * The European Union's AI Act development process could benefit from GRAPHYP in allowing policymakers to navigate the complex landscape of AI ethics debates and inform their regulatory decisions. The European Organization for Nuclear Research (CERN) could implement our framework to guide their research priorities and communicate the state of particle physics knowledge to the scientific community and the public. In all those cases, the DL framework could provide a tool for interactive dynamic interaction: indeed, as structured from human practices of predecessors, it has the objective nature of a commonwealth that is not subject to manipulative destination in the present space of fake news. The result, in GRAPHYP, is a dynamic common good. * **GRAPHYP Search Interface: Color Coding and Sandbox Testing**  
Color coding is an important domain of standardization in DL as it allows for direct visual access to knowledge, with additional strategic consequences. Basic symbols could be easily settled, with the classification of disputable knowledge options. On that topic, we deliver a separate study: in Appendix B, we present the results of a query documenting the very large array of analytic attributes of a colored design of GRAPHYP network results. By coupling this color-coding system with the features of augmentation and categorizations in dispute learning, a powerful visual tool for analyzing disputable knowledge is created, which provides an instantaneous calibration of difficult debates to all categories of potential users. We consider the correlative importance of installing sandboxes for testing DL analyses and results in the context of open access. * **Dispute Analysis Outcomes: Novel Cross-Representations of Adversarial Positions**  
Dispute-based knowledge (DBK) represents a novel result type emerging from GRAPHYP's geometric adversarial morphology. It enables, by cut-and-paste practices, automated creation of crossed representations of adversarial human perspectives--analogous to how crossing negatives can result in a positive. The general workflow of dispute-based knowledge can be illustrated through the Conflicting Conditions and Crossed Explorations (CCCE) framework, which explores uncharted knowledge within an explainable AI context, fundamentally distinguishing itself from current generative AI production.