Christina Dienhart

Patent classifications are central to technology and innovation research, providing structured yet detailed technological information. However, the hierarchical design of widely used systems such as IPC and CPC imposes three long-standing constraints: limited horizontal comparability within domains, the separation of technologies into siloed domains, and inconsistencies in classification across patent office jurisdictions. The contribution of this paper is threefold: Firstly, we address these constraints conceptually and methodologically by developing a machine learning method, based on Meta’s Llama 3, that assigns Japanese F-terms - granular, attribute-level descriptors - to patents worldwide, and simultaneously generates high-dimensional vector representations that map all F-terms into a unified technological space. Secondly, we operationalize this capability by releasing both the trained classifier and the complete vector dataset, enabling researchers and practitioners to apply F-term–based methods beyond Japan, to conduct consistent cross-national analyses, and to measure technological distances across the entire innovation landscape. Thirdly, we outline a research agenda that leverages these new resources to advance the study of technological change, innovation systems, and cross-domain analysis. While the classifier’s performance can be further enhanced, this work provides a new empirical basis for studying technological change and innovation with greater granularity, cross-domain comparability, and consistency across jurisdictions.

When common‐pool resources such as freshwater, clean air, and fisheries span national borders, the collective action problems encountered are particularly severe. This study explores the role of polycentric governance systems in overcoming these pressing problems, which frequently underlie global grand challenges. Integrating political economy and management research, we hypothesize about how two governance mechanisms – international treaties and multi‐stakeholder organizations – shape the likelihood of cooperation and conflict between countries. Leveraging unique, longitudinal data capturing interactions of countries bordering international river basins, our empirical analysis reveals two main findings. First, we find that the specification of multi‐stakeholder organizations enhances water‐related cooperation and reduces water conflict among countries, while the specification of international treaties enhances cooperation but does not affect conflict. Second, we find that leaving one of these governance mechanisms less specified than the other actually improves, rather than harms, relationships between countries. Our findings point to a superior governance configuration that simultaneously enhances cooperation and constrains conflict. This configuration combines: (1) treaties that establish property rights but leave procedural rules and uncertainty management provisions less established with (2) multi‐stakeholder organizations that define processes for making decisions, sharing information, engaging the public, and resolving disputes.