Hybrid Knowledge and the Historiography of Science

Originating in the field of biology, the concept of the hybrid has proved to be influential and effective in historical studies, too. Until now, however, the idea of hybrid knowledge has not been fully explored in the historiography of pre-modern science. This article examines the history of pre-Copernican astronomy and focuses on three case studies—Alexandria in the second century CE; Baghdad in the ninth century; and Constantinople in the fourteenth century—in which hybridization played a crucial role in the development of astronomical knowledge and in philosophical controversies about the status of astronomy and astrology in scholarly and/or institutional settings. By establishing a comparative framework, this analysis of hybrid knowledge highlights different facets of hybridization and shows how processes of hybridization shaped scientific controversies.

Enhanced Language Model with Hybrid Knowledge Graph for Mathematical Topic Prediction

Understanding mathematical topics is important for both educators and students to capture latent concepts of questions, evaluate study performance, and recommend content in online learning systems. Compared to traditional text classification, mathematical topic classification has several main challenges: (1) the length of mathematical questions is relatively short; (2) there are various representations of the same mathematical concept(i.e., calculations and application); (3) the content of question is complex including algebra, geometry, and calculus. In order to overcome these problems, we propose a framework that combines content tokens and mathematical knowledge concepts in whole procedures. We embed entities from mathematics knowledge graphs, integrate entities into tokens in a masked language model, set up semantic similarity-based tasks for next-sentence prediction, and fuse knowledge vectors and token vectors during the fine-tuning procedure. We also build a Chinese mathematical topic prediction dataset consisting of more than 70,000 mathematical questions with topics. Our experiments using real data demonstrate that our knowledge graph-based mathematical topic prediction model outperforms other state-of-the-art methods.

A Hybrid Knowledge-Based Recommender for Product-Service Systems Mass Customization

Manufacturers today compete to offer not only products, but products accompanied by services, which are referred to as product-service systems (PSSs). PSS mass customization is defined as the production of products and services to meet the needs of individual customers with near-mass-production efficiency. In the context of the PSS mass customization environment, customers are overwhelmed by a plethora of previously customized PSS variants. As a result, finding a PSS variant that is precisely aligned with the customer’s needs is a cognitive task that customers will be unable to manage effectively. In this paper, we propose a hybrid knowledge-based recommender system that assists customers in selecting previously customized PSS variants from a wide range of available ones. The recommender system (RS) utilizes ontologies for capturing customer requirements, as well as product-service and production-related knowledge. The RS follows a hybrid recommendation approach, in which the problem of selecting previously customized PSS variants is encoded as a constraint satisfaction problem (CSP), to filter out PSS variants that do not satisfy customer needs, and then uses a weighted utility function to rank the remaining PSS variants. Finally, the RS offers a list of ranked PSS variants that can be scrutinized by the customer. In this study, the proposed recommendation approach was applied to a real-life large-scale case study in the domain of laser machines. To ensure the applicability of the proposed RS, a web-based prototype system has been developed, realizing all the modules of the proposed RS.

The Dialectic of Transnational Integration and National Disintegration as Challenge for Multilevel Governance

A large number of studies have detected that within the EU multilevel governance there is a transformation toward a hybrid knowledge co-production that overcomes traditional categories such as locality or embeddedness. There, a sort of sustainable decision-making knowledge is co-developed and theoretically supposed to be applied top-down on the national level of EU member states. However, in practice such processes of unification are always associated with a risk of limited compliance with specific national situations and with a specific national “world of relevancies”. Despite the rise in popularity of these top-down initiatives within international policy levels, there is a lack of studies that empirically analyze how national policy systems respond to these global standardization approaches. Therefore, the central aim of this study is twofold: Based on an exemplary case of an international information system co-produced by an expert group of the European Commission, it first reconstructs whether and how transnational information is integrated on the national policy level. Second, it elucidates factors limiting an application. The results show that this international knowledge system was used for basal purposes and was mainly challenged by non-compliance with national specificities and the existence of alternative knowledge sources.

More than a method: trusting relationships, productive tensions, and two-way learning as mechanisms of authentic co-production

Background Knowledge mobilisation requires the effective elicitation and blending of different types of knowledge or ways of knowing, to produce hybrid knowledge outputs that are valuable to both knowledge producers (researchers) and knowledge users (health care stakeholders). Patients and service users are a neglected user group, and there is a need for transparent reporting and critical review of methods used to co-produce knowledge with patients. This study aimed to explore the potential of participatory codesign methods as a mechanism of supporting knowledge sharing, and to evaluate this from the perspective of both researchers and patients. Methods A knowledge mobilisation research project using participatory codesign workshops to explore patient involvement in using health data to improve services. To evaluate involvement in the project, multiple qualitative data sources were collected throughout, including a survey informed by the Generic Learning Outcomes framework, an evaluation focus group, and field notes. Analysis was a collective dialogic reflection on project processes and impacts, including comparing and contrasting the key issues from the researcher and contributor perspectives. Results Authentic involvement was seen as the result of “space to talk” and “space to change”. "Space to talk" refers to creating space for shared dialogue, including space for tension and disagreement, and recognising contributor and researcher expertise as equally valuable to the discussion. ‘Space to change’ refers to space to adapt in response to contributor feedback. These were partly facilitated by the use of codesign methods which emphasise visual and iterative working, but contributors emphasised that relational openness was more crucial, and that this needed to apply to the study overall (specifically, how contributors were reimbursed as a demonstration of how their input was valued) to build trust, not just to processes within the workshops. Conclusions Specific methods used within involvement are only one component of effective involvement practice. The relationship between researcher and contributors, and particularly researcher willingness to change their approach in response to feedback, were considered most important by contributors. Productive tension was emphasised as a key mechanism in leading to genuinely hybrid outputs that combined contributor insight and experience with academic knowledge and understanding.