Cross-Domain Knowledge Integration Framework for Interdisciplinary Scientific Innovation
Keywords:
interdisciplinary innovation; knowledge integration; socio-technical infrastructure; data governance; boundary objects; FAIR; responsible AI; research ecosystemsAbstract
Interdisciplinary scientific innovation increasingly depends on the capacity of research ecosystems to integrate knowledge across heterogeneous domains, institutions, and socio-technical settings. Yet most integration efforts remain brittle: they overemphasize technical interoperability while under-specifying governance, incentives, and the infrastructural conditions that make integration sustainable at scale. This paper develops a system-level framework for cross-domain knowledge integration oriented toward interdisciplinary innovation under real-world constraints. We treat integration as an architectural and institutional problem rather than merely a representational one, and we synthesize insights from infrastructure studies, organization theory, science and technology studies, information governance, and responsible AI. The framework is organized around four coupled layers: epistemic alignment, boundary mediation, infrastructural interoperability, and governance and accountability. Across layers, we analyze structural trade-offs involving standardization versus pluralism, centralization versus federation, openness versus security, and velocity versus reliability. We illustrate how the framework informs deployment pathways in research consortia, data commons, and AI-enabled discovery platforms, emphasizing robustness, fairness, and long-horizon sustainability. We argue that successful interdisciplinary integration requires explicitly designed boundary objects and coordination mechanisms, machine-actionable stewardship principles, and risk-based governance that can adapt to shifting scientific, regulatory, and geopolitical environments. The paper concludes with forward-looking implications for evaluating integration quality and for building durable socio-technical infrastructures that enable innovation without eroding trust, equity, or scientific integrity.
References
1.Bechky, B. A. (2003). Sharing meaning across occupational communities: The transformation of understanding on a production floor. Organization Science, 14(3), 312–330.
2.Bijker, W. E., Hughes, T. P., & Pinch, T. J. (Eds.). (1987). The social construction of technological systems: New directions in the sociology and history of technology. MIT Press.
3.Bowker, G. C., & Star, S. L. (1999). Sorting things out: Classification and its consequences. MIT Press.
4.Carlile, P. R. (2002). A pragmatic view of knowledge and boundaries: Boundary objects in new product development. Organization Science, 13(4), 442–455.
5.Caccamo, M. (2023). Boundary objects, knowledge integration, and innovation: A review and research agenda. Technovation, 123, 102724.
6.Edwards, P. N. (2010). A vast machine: Computer models, climate data, and the politics of global warming. MIT Press.
7.Feenberg, A. (1991). Critical theory of technology. Oxford University Press.
8.Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: The dynamics of science and research in contemporary societies. SAGE.
9.Griesemer, J. R., & Star, S. L. (1989). Institutional ecology, “translations” and boundary objects: Amateurs and professionals in Berkeley’s Museum of Vertebrate Zoology, 1907–39. Social Studies of Science, 19(3), 387–420.
10.Hughes, T. P. (1983). Networks of power: Electrification in Western society, 1880–1930. Johns Hopkins University Press.
11.Jasanoff, S. (2004). States of knowledge: The co-production of science and the social order. Routledge.
12.Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. SAGE.
13.Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford University Press.
14.Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.
15.National Institute of Standards and Technology. (2023). AI Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce.
16.Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press.
17.Ostrom, E. (1990). Governing the commons: The evolution of institutions for collective action. Cambridge University Press.
18.Popper, K. (1959). The logic of scientific discovery. Hutchinson.
19.Porter, T. M. (1995). Trust in numbers: The pursuit of objectivity in science and public life. Princeton University Press.
20.Ribes, D., & Finholt, T. A. (2009). The long now of technology infrastructure: Articulating tensions in development. Journal of the Association for Information Systems, 10(5), 375–398.
21.Shapin, S. (1994). A social history of truth: Civility and science in seventeenth-century England. University of Chicago Press.
22.Star, S. L. (1999). The ethnography of infrastructure. American Behavioral Scientist, 43(3), 377–391.
23.Suchman, L. (2007). Human-machine reconfigurations: Plans and situated actions (2nd ed.). Cambridge University Press.
24.Timmermans, S., & Berg, M. (2003). The gold standard: The challenge of evidence-based medicine and standardization in health care. Temple University Press.
25.Van de Ven, A. H. (2007). Engaged scholarship: A guide for organizational and social research. Oxford University Press.
26.Weinberg, A. M. (1961). Impact of large-scale science on the United States. Science, 134(3473), 161–164.
27.Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J. W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018.
28.Woolgar, S., & Latour, B. (1979). Laboratory life: The construction of scientific facts. SAGE.
29.Zhang, J., et al. (2025). Developing a conceptual framework for interdisciplinary research as a boundary object. [Article in PubMed Central].
30.Reuters. (2024, September 19). UN advisory body makes seven recommendations for governing AI.
31.Wang, Y. (2025, April). Efficient adverse event forecasting in clinical trials via transformer-augmented survival analysis. In Proceedings of the 2025 International Symposium on Bioinformatics and Computational Biology (pp. 92-97).
32.Wang, Y. (2025, June). RAGNet: Transformer–GNN–Enhanced Cox–Logistic Hybrid Model for Rheumatoid Arthritis Risk Prediction. In Proceedings of the 2025 International Conference on Health Informatization and Data Analytics (pp. 90-94).
33.Yi, X. (2025, October). Real-Time Fair-Exposure Ad Allocation for SMBs and Underserved Creators via Contextual Bandits-with-Knapsacks. In Proceedings of the 2025 2nd International Conference on Digital Economy and Computer Science (pp. 1602-1607).
34.Tang, Y., Kojima, K., Gotoda, M., Nishikawa, S., Hayashi, S., Koike-Akino, T., ... & Klamkin, J. (2020, February). InP grating coupler design for vertical coupling of InP and silicon chips. In Integrated Optics: Devices, Materials, and Technologies XXIV (Vol. 11283, pp. 33-38). SPIE.
35.Li, B. (2025). GIS-Integrated Semi-Supervised U-Net for Automated Spatiotemporal Detection and Visualization of Land Encroachment in Protected Areas Using Remote Sensing Imagery.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
