Editor’s Note: This post is part of our Big Ideas Series, a column highlighting the innovative thinking and thought leadership at IIeX events around the world. Stu Shulman will be speaking at IIeX North America (June 13-15 in Atlanta). If you liked this article, you’ll LOVE IIeX NA. Click here to learn more.
By Stu Shulman
CoderRank is a big idea. CoderRank is to text analytics what Google’s PageRank has been to search. Just as Google said not all web pages are created equal, links on some pages rank higher than others, I argue that not all human coders are created equal; the accuracy of observations by some coders invariably rank higher than others.
The major idea is that when training machines for text analysis, greater reliance should be placed on the specific inputs of those humans most likely to create a valid observation. I proposed a unique way to measure and rank humans on trust and knowledge vectors, and called it CoderRank. The U.S. Patent and Trademark Office agreed it was a novel approach to machine-learning and issued a patent March 1, 2016. Not bad for a political scientist.
In 2011 I read a few very important and influential books. These books brought years of laboratory experiments into sharper focus contributing directly to the big idea. One was What Would Google Do?: Reverse-Engineering the Fastest Growing Company in the History of the World by Jeff Jarvis. I already knew about PageRank and the history of search technology through other books; however, Jarvis introduced me to a compelling way to think about where value is created in distributed software systems. What Google does is let end users and builders of systems create value on top of their web-based infrastructure.
Another source of inspiration for CoderRank was Everything Is Miscellaneous: The Power of the New Digital Disorder by David Weinberger. Weinberger writes compellingly about the difference between organizing books in a library using the rigid Dewey Decimal system versus the way we filter information in online databases using different observations, different people, different systems, and influenced by different reasons. A take-away point, however, is that every observation matters, but some matter more depending on the context.
There is no more important book in the formation of this big idea than James Gleick’s The Information: A History, A Theory, A Flood. The story of information conceptualization is fascinating. In every epoch, the innovators built new tools for collecting, measuring, and processing information. From Plato’s deep concerns about the frustrating effects of categorization disagreements, through the dawn of machine-learning, Gleick surfaces fundamental problems with information management. The problems with categorization cannot easily be ignored or planned out of existence. However, identifying the best tools, methods, and measurements fits squarely in the long history of information.
The big idea of CoderRank builds on these known experimental and theoretical problems. It suggests a universal law: for every categorization problem, some humans will do better than others. How we deal with this fact is a challenge for data scientists and qualitative researchers going forward.