Knowledge filters

This graphic is part of the Seek-Sense-Share PKM model and is based on Five forms of filtering by Tim Kastelle. Here’s a review of the five forms.

Naive filtering is what too often happens in our knowledge searching. It’s like prairie-dogging, or standing up in your cubicle and asking those close to you for advice. It’s rather hit and miss and dependent on who works nearby and happens to be listening.

Expert filtering worked when knowledge was more stable but in an interconnected, interdependent, digital world we have to ask, who are the experts? Still, good experts are valuable and I use platforms like Twitter to connect to them, like Michael Geist on Canadian copyright law or Valdis Krebs on networks.

Networked expertise can be sought through group-sourced information resources, like our curated Working Smarter Daily or in self-created expertise lists like Google+ to create circles of expertise. You can also link to existing communities of expertise/interest such as KMers on knowledge management.

Algorithmic filters can be simple, like typing in a basic search string, or more refined using techniques like Google’s advanced operators.

A good perspective on Heuristic filters is Howard Rheingold’s Crap Detection Skills:

Unless a great many people learn the basics of online crap detection and begin applying their critical faculties en masse and very soon, I fear for the future of the Internet as a useful source of credible news, medical advice, financial information, educational resources, scholarly and scientific research. Some critics argue that a tsunami of hogwash has already rendered the Web useless. I disagree. We are indeed inundated by online noise pollution, but the problem is soluble. The good stuff is out there if you know how to find and verify it. Basic information literacy, widely distributed, is the best protection for the knowledge commons: A sufficient portion of critical consumers among the online population can become a strong defense against the noise-death of the Internet.

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