Findings and Implications from Data Mining the IMC Review Process
Title | Findings and Implications from Data Mining the IMC Review Process |
Publication Type | Journal Article |
Year of Publication | 2013 |
Authors | Beverly, R., & Allman M. |
Published in | ACM SIGCOMM Computer Communication Review |
Volume | 3 |
Issue | 1 |
Page(s) | 23-29 |
Other Numbers | 3410 |
Abstract | The computer science research paper review process is largely human and time-intensive. More worrisome, review processes are frequently questioned, and often non-transparent. This work advocates applying computer science methods and tools to the computer science review process. As an initial exploration, we data mine the submissions, bids, reviews, and decisions from a recent top-tier computer networking conference. We empirically test several common hypotheses, including the existence of readability, citation, call-for-paper adherence, and topical bias. From our findings, we hypothesize review process methods to improve fairness, efficiency, and transparency. |
URL | http://www.icsi.berkeley.edu/pubs/networking/findingsimplications12.pdf |
Bibliographic Notes | ACM SIGCOMM Computer Communication Review, Vol. 3, No. 1, pp. 23-29 |
Abbreviated Authors | R. Beverly and M. Allman |
ICSI Research Group | Networking and Security |
ICSI Publication Type | Article in journal or magazine |