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Counting citations hasn't been a reliable measure of scientific impact for a while, especially on platforms like Google Scholar that compile info from random documents. Hyper-authorship, predatory journals, etc have all contributed to the problem.
This preprint just drives home how important it is to measure scientific impact more carefully and without reliance on automated metrics
Google Scholar is manipulatable
https://arxiv.org/abs/2402.04607
#science #academia #publishing #citations
Google Scholar is manipulatable
Citations are widely considered in scientists' evaluation. As such, scientists may be incentivized to inflate their citation counts.arXiv.org
New study: "We find that scientists are overwhelmingly (95%) failing to publish their #code and that there has been no significant improvement over time, but we also find evidence that code sharing can considerably improve #citations, particularly when combined with #OpenAccess publication."
https://www.researchsquare.com/article/rs-3222221/v1
#Impact #OACA #OpenSource #Reproducibility
Code sharing increases citations, but remains uncommon
Biologists increasingly rely on computer code, reinforcing the importance of published code for transparency, reproducibility, training, and a basis for further work.www.researchsquare.com
https://arxiv.org/abs/2306.09911
Uncited articles and their effect on the concentration of citations
Empirical evidence demonstrates that citations received by scholarly publications follow a pattern of preferential attachment, resulting in a power-law distribution.arXiv.org