Findings and implications from data mining the IMC review process

Robert Beverly · Mark C. Allman

6 citations0/0 files storedACM SIGCOMM Computer Communication Review

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scientometrics and bibliometrics research Academic Writing and Publishing Academic Publishing and Open Access

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.

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1988Thirteen Ways to Look at the Correlation Coefficient 2009ViewpointProgram committee overload in systems 2008ViewpointScaling the academic publication process to internet scale 2005Experiences from the Sigcomm 2005 European shadow PC experiment 2008What ought a program committee to do 2011Rebooting the CS publication process 2008Thoughts on reviewing 2007Author feedback experiment at PAM 2007

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