Grand challenges and emergent modes of convergence science
Alexander M. Petersen, Mohammed E. Ahmed, Ioannis Pavlidis

TL;DR
This paper examines how different modes of convergence in brain science research impact knowledge integration and influence scientific impact, revealing that shortcut methods are growing faster but less effective, potentially due to funding incentives.
Contribution
It provides an empirical analysis of convergence modes in brain science, highlighting the rise of shortcut approaches and their implications for research impact and policy.
Findings
Cross-disciplinary collaboration yields a 16% citation premium.
Shortcut convergence methods are increasing at 3% annually.
Policy pressures may incentivize less effective convergence strategies.
Abstract
To address complex problems, scholars are increasingly faced with challenges of integrating diverse knowledge domains. We analyzed the evolution of this convergence paradigm in the broad ecosystem of brain science, which provides a real-time testbed for evaluating two modes of cross-domain integration - subject area exploration via expansive learning and cross-disciplinary collaboration among domain experts. We show that research involving both modes features a 16% citation premium relative to a mono-disciplinary baseline. Further comparison of research integrating neighboring versus distant research domains shows that the cross-disciplinary mode is essential for integrating across relatively large disciplinary distances. Yet we find research utilizing cross-domain subject area exploration alone - a convergence shortcut - to be growing in prevalence at roughly 3% per year, significantly…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsScientific Computing and Data Management · Bioinformatics and Genomic Networks
