Name
Abstract | ||
|---|---|---|
The increasing volume of scientific publications and grant proposals has generated an unprecedentedly high workload to scientific communities. Consequently, review quality has been decreasing and review outcomes have become less correlated with the real merits of the papers and proposals. A novel distributed peer review (DPR) approach has recently been proposed to address these issues. The new approach assigns principal investigators (PIs) who submitted proposals (or papers) to the same program as reviewers. Each PI reviews and ranks a small number (such as seven) of other PIsu0027 proposals. The individual rankings are then used to estimate a global ranking of all proposals using the Modified Borda Count (MBC). In this study, we perform simulation studies to investigate several parameters important for the decision making when adopting this new approach. We also propose a new method called Concordance Index-based Global Ranking (CIGR) to estimate global ranking from individual rankings. An efficient simulated annealing algorithm is designed to search the optimal Concordance Index (CI). Moreover, we design a new balanced review assignment procedure, which can result in significantly better performance for both MBC and CIGR methods. We found that CIGR performs better than MBC when the review quality is relatively high. As review quality and review difficulty are tightly correlated, we constructed a boundary in the space of review quality vs review difficulty that separates the CIGR-superior and MBC-superior regions. Finally, we propose a multi-stage DPR strategy based on CIGR, which has the potential to substantially improve the overall review performance while reducing the review workload. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Digital Libraries | Small number,Simulated annealing,Data mining,Borda count,Ranking,Workload,Computer science,Concordance |
DocType | Volume | Citations |
Journal | abs/1806.08663 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Albert Steppi | 1 | 6 | 1.17 |
| Jinchan Qu | 2 | 0 | 0.34 |
| Minjing Tao | 3 | 0 | 0.34 |
| Tingting Zhao | 4 | 5 | 1.75 |
| Xiaodong Pang | 5 | 2 | 2.04 |
| Jinfeng Zhang | 6 | 86 | 10.11 |