2013/11/11 by Bradly Alicea, Alicea, Bradly
Computer Science · Decision Sciences · Physics and Astronomy · #Digital Libraries (cs.DL) #Expert finding and Q&A systems #FOS: Computer and information sciences #FOS: Physical sciences #Human-Computer Interaction (cs.HC) #Physics and Society (physics.soc-ph) #Scientific Computing and Data Management #Social and Information Networks (cs.SI) #cs.DL #cs.HC #cs.SI #physics.soc-ph #scientometrics and bibliometrics research
paper · pdf · doi:10.48550/arxiv.1311.2504
6 pages, 2 figures, 1 table. Associated code an pseudo-code at: https://github.com/balicea/semi-auto-peer-review
arxiv created 2013/11/11 · openalex publication_date 2013/11/11 · arxiv updated 2013/11/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A semi-supervised model of peer review is introduced that is intended to overcome the bias and incompleteness of traditional peer review. Traditional approaches are reliant on human biases, while consensus decision-making is constrained by sparse information. Here, the architecture for one potential improvement (a semi-supervised, human-assisted classifier) to the traditional approach will be introduced and evaluated. To evaluate the potential advantages of such a system, hypothetical receiver operating characteristic (ROC) curves for both approaches will be assessed. This will provide more specific indications of how automation would be beneficial in the manuscript evaluation process. In conclusion, the implications for such a system on measurements of scientific impact and improving the quality of open submission repositories will be discussed.