KMbeing

Knowledge Mobilization (KMb): Multiple Contributions & Multi-Production Of New Knowledge

Tag Archives: Krista Jensen

Relevant-Signal To Data-Noise Ratio

signal noise

In science and engineering we often hear about the signal to noise ratio – a concept that compares the level of a desired signal to the level of background noise.  Although this is a technical term commonly used for electrical signals or biochemical signaling between cells, it can also be applied in the world of social media. In my own social media use I call this relevant-signal to data-noise ratio.

How often do we sift through Twitter feeds or Google search results to find what is relevant to our online research while also being inundated with data-noise?  I always keep this in mind when I’m doing digital research.  I can often find my Twitter feed filled with tweets that are relevant to digital research – and plenty more that are simply data-noise. Understanding the social media concept of relevant-signal to data-noise ratio can help us use social media in a more effective and productive manner and keep us focused on the more relevant information and knowledge sharing that makes using social media – especially for knowledge mobilization (KMb) – a better and more valuable experience.

As a community-based digital researcher, I was involved in a research project and book chapter publication with the Knowledge Mobilization Unit at York University, working with York University’s Executive Director of Research & Innovation Services,  Dr. David Phipps and York’s KMb knowledge broker,  Krista Jensen.  Our research project looked at Applying Social Sciences Research for Public Benefit Using Knowledge Mobilization and Social Media.  One of my contributions to this project was analyzing online profile keywords used on Twitter to advance our understanding of how individuals might use a social media platform like Twitter to connect and form collaborative relationships and like interests. Like interests are the foundation of communities of practice.

This important concept of relevant-signal to data-noise ratio  can be conceptualized by the following equation:

R-S:D-N = A (amount) of relevant-signal

                 = A (amount) of data-noise = 50

Basically, what this formula means is that the relevant-signal to data-noise ratio is equal to the average amount of what is a relevant-signal divided by what is the average amount of data-noise. To use this equation, for example, on a Twitter feed of someone I’m following on Twitter, I will often seek the keywords that are relevant to my digital research on a page of profile tweets. This can easily be done using the Ctrl-F Find function on any computer. I type in the keywords I’m looking for and – for convenience sake – I hold the amount of data-noise is going to be at least half or fifty-percent – as in a 50-50 chance.  This is why I have the amount of data-noise equal to 50.

When I find my keywords at least twenty-five-percent (25%) of the time or more (at least half of my 50-50 chance of finding data-noise), I will continue to follow this Twitter feed. If the amount is less than 25% – it’s filled with too much data-noise for what is relevant to my research interests, and I often make the decision to un-follow. I find this equation very helpful in making decisions about who to follow by weeding-out more of the data-noise.

All real measurement is disturbed by noise – and social media is no exception. As a research tool, social media is now being recognized as a valid part of gathering, exchanging and creating new knowledge, and as part of doing valid research.  However, many are still not effectively using social media in the best possible way to do this, and are still being swamped by a deluge of information and data-noise not relevant to knowledge sharing interests.  Or worse, people feel they need to connect broadly so as not to “miss anything”.  Remember, social media is NOT a popularity contest.  Attempts to measure or analyze your online success with what can be called as vanity metrics is irrelevant. It’s quality NOT quantity that counts in social media – so you may have to un-follow and eliminate some of that data-noise to find the relevant signal. I hope this relevant-signal to data-noise ratio equation is helpful for you in this process.

Knowledge Mobilization (KMb) Clear Language Research Field Note: My Contribution

I’m excited to announce the recent publication and my contribution as co-author of a Field Note Describing the Development and Dissemination of Clear Language Research Summaries for University-Based Knowledge Mobilization

Along with Dr. David Phipps, the Director of Research Services and Knowledge Exchange at York University in Toronto, and Krista Jensen, York University’s Knowledge Mobilization Officer,  and Michael Johnny, Manager, Knowledge Mobilization at York University, I was privileged to be part of the research and writing team. It was a great honour to work with David, Krista and Michael – especially during my time volunteering in the Knowledge Mobilization (KMb) Unit at York University.

David, Krista and I also had a recent publication in which I was able to contribute as a co-author of an In-Tech Book Chapter entitled Applying Social Sciences Research for Public Benefit Using Knowledge Mobilization and Social Media.

As a Community-based participant and contributor to Knowledge Mobilization (KMb) it is my hope that these works will help promote the continued use of KMb for everyone to make the world a better place for everyone.

Knowledge Mobilization (KMb) Book Chapter: My Contribution

I’m excited to announce the recent publication and my contribution as co-author of an In-Tech Book Chapter entitled "Applying Social Sciences Research for Public Benefit Using Knowledge Mobilization and Social Media". Along with Dr. David Phipps, the Director of Research Services and Knowledge Exchange at York University in Toronto, and Krista Jensen, York University’s Knowledge Mobilization Officer, I was privileged to be part of the research and writing team.

As a community-based Knowledge Mobilizer, my contribution focused on the literature review and Twitter research project data collection and statistical analysis, along with my development and presentation of the Knowledge Mobilization (KMb) model of sector interaction for social benefit – which I first published in my KMbeing blog post in January 2011.

 

It was a great honour to work with both David and Krista. David has also recently had another paper published of his collaboration with one of Knowledge Mobilization’s foremost experts in research utilization, Sandra Nutley (unfortunately, the paper is not available in open access yet – but a link to the paper is found here). Nutley is co-author of Using Evidence: How research can inform public services

Krista Jensen’s expertise on the use of social media, and her background in library science is of great value in her contribution to the literature review and book chapter writing about the use of social media for Knowledge Mobilization - and compliments my own practical use of social media, such as Twitter – along with my KMbeing blog to promote Knowledge Mobilization for social benefit.

The In-Tech book chapter is available to everyone in open access online. Our book chapter addresses the importance of social sciences for academics and non-academics - in research institutions and at the community level, and the important role of knowledge brokers - to address wicked problems, and enhance the research process and sharing of knowledge with the use of social media. It is my hope that it will promote the continued use of Knowledge Mobilization (KMb) to make the world a better place for everyone.

 

Merry Knowledge Mobilization (KMb)

I recently took to the ice to teach some knowledge brokers from York University’s KMb Unit how to curl – very, very basic lessons like how not to fall flat on your ass when on the ice.  It was part of their annual KMb Summit.

Curling – like knowledge mobilization (sharing knowledge for social benefit) – is another of my hobbies in life.

To all of my dedicated KMbeing blog followers and to new followers – I wish you all the very best for the holiday season and all the very best for knowledge mobilization in 2012!

And from KMbeing.com !

Knitting Knowledge Mobilization

Do you knit? Do you like to turn yarn or thread into warm, comfortable clothing or snuggly blankets? I’d like to dedicate this blog to all of the knitters out there. Quite surprisingly, for some reason, I’ve found many of my colleagues in Knowledge Mobilization (KMb) are genuine “dyed-in-the-wool” regular knitters. Is there some sort of strange connection between people who enjoy knitting and knowledge mobilization? Probably not. However, it’s suspicious that both knitting and knowledge both begin with silent Ks! All humour aside, it appears that knitting remains a very popular hobby.

One of my dedicated blog and Twitter followers (whom I also follow regularly) is Bonnie Zink (on Twitter @BonnieZink). Bonnie is a writer, editor and a knowledge translation & exchange specialist, as well as being a social media enthusiast interested in Knowledge Mobilization. Her Twitter profile says she “loves to indulge in knitterly obsessions” which is clearly seen in Bonnie’s blog Stitching in Saskatoon.

Bonnie’s knitting blog is so popular that this past weekend, Bonnie tweeted: “A new “record!” Over 400 reads of the blog this weekend. Thanks! I take it as a sign that you enjoy what I #write. http://bit.ly/8oDKK4”.

Apparently there are more hits to knitting blogs than knowledge mobilization blogs! Why do you suppose that is? I’ve checked with many of my fellow KMb bloggers and they admit they rarely come close to that number on any weekend or daily level. Congrats Bonnie!

Another Twitter follower (and whom I also follow regularly)  is an educator, academic career coach and regular knitter is Jo Vanevery http://jovanevery.ca/ (on Twitter @jovanevery). Jo’s postings are always thought-provoking and helpful to those seeking academic direction, guidance and information. Amusingly, Jo continues to pepper several of her enjoyable academic blog posts with mentions of knitting.

And knitting is also the hobby of two of Canada’s top knowledge brokers,  Michael Johnny (on Twitter @mobilizemichael), and  Krista Jensen (on Twitter @atomickitty), whom I work with at the Knowledge Mobilization Unit at York University – and also part of ResearchImpact, Canada’s Knowledge Mobilization Network. I’ve never seen them knitting at work, but both tell me it’s a leisurely pleasure they enjoy. (To see some of Krista’s knitting projects link here http://www.flickr.com/photos/86079743@N00/sets/72157622667413246/). There’s even a social networking site for knitters and crocheters called Ravelry.

Although I’m not a knitter, I did a little research. Did you know that originally, knitting was a male-only occupation? The first knitting trade guild was started in Paris in 1527. Today, thankfully and rightfully, it’s good to see woman are now as included in formally contributing knowledge as they are in knitting. It’s also good to see that men are also still knitting. I’ve been known to see a KMb connection in almost anything to help explain what Knowledge Mobilization is. So here goes with knitting…

You probably know that the yarn in knitted fabrics follows a meandering path, forming symmetrical loops around a path of yarn. These meandering loops can be stretched easily in different directions, giving knitting much more elasticity (and strength) than many woven fabrics. Depending on the yarn and knitting pattern, knitted garments can stretch as much as 500%. There are also many hundreds of different knitting stitches used by knitters, and different ways to insert the needle into the stitch.

So let’s say Knowledge Mobilization (KMb) is a process like knitting. Just as knitting relies on the continuous stitching of symmetrical loops, KMb relies on the continuous action loops of informing and being informed. As each stitch is knitted and stretched in different directions, so too is knowledge expanded when turned into action through mobilization.

Like the many types of stitching with many different types of threads, KMb includes many different knowledge sectors and individuals (cultures, communities, beliefs, academia, organizations, associations) brought together to be woven into a valuable knowledge fabric for the benefit of society.

My analogy might be a stretch (oh groan!), but like a path of yarn, knitting and knowledge through focused stitching and mobilization can create value that can move, extend and provide something good for others.

And as knitter and knowledge mobilizer Bonnie Zink says… “Happy stitching!”

A Career in Knowledge Mobilization

Translating Knowledge Across Sectors featuring David Phipps and Krista Jensen from ResearchImpact at York University, Toronto.

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