Cautious explorers generate more future academic impact
June 29, 2023 Β· Declared Dead Β· π Social Science Research Network
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Xingsheng Yang, Zhaoru Ke, Qing Ke, Haipeng Zhang, Fengnan Gao
arXiv ID
2306.16643
Category
cs.DL: Digital Libraries
Cross-listed
cs.SI,
physics.soc-ph
Citations
0
Venue
Social Science Research Network
Last Checked
3 months ago
Abstract
Some scientists are more likely to explore unfamiliar research topics while others tend to exploit existing ones. In previous work, correlations have been found between scientists' topic choices and their career performances. However, literature has yet to untangle the intricate interplay between scientific impact and research topic choices, where scientific exploration and exploitation intertwine. Here we study two metrics that gauge how frequently scientists switch topic areas and how large those jumps are, and discover that 'cautious explorers' who switch topics frequently but do so to 'close' domains have notably better future performance and can be identified at a remarkably early career stage. Cautious explorers who balance exploration and exploitation in their first four career years have up to 19% more citations per future paper. Our results suggest that the proposed metrics depict the scholarly traits of scientists throughout their careers and provide fresh insight, especially for nurturing junior scientists.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Digital Libraries
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Measuring academic influence: Not all citations are equal
R.I.P.
π»
Ghosted
The Open Access Advantage Considering Citation, Article Usage and Social Media Attention
R.I.P.
π»
Ghosted
A Bibliometric Review of Large Language Models Research from 2017 to 2023
R.I.P.
π»
Ghosted
On the Performance of Hybrid Search Strategies for Systematic Literature Reviews in Software Engineering
R.I.P.
π»
Ghosted
A Systematic Identification and Analysis of Scientists on Twitter
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted