Navigating Social Capital Scholarship
Did you know that the Web of Science Core Collection (WoS) contains fifty-seven million academic papers, books, conference proceedings, and citations across the social and natural sciences? Today in that database there are more than 14,000 research records about "social capital," a core Cardus concern.
Don't worry, we've undertaken a thorough review of the resources to help map the terrain of the research available to you.
Despite uneven adoption of social capital research at a policy level, this growing body of resources reflects the importance of applying academic work done on social capital to our civic sphere.
Why not take a minute today or this coming weekend to measure the economic impact that religious organizations in your hometown make? Simply pull up the Halo Calculator on your phone and type in your city's name!
Thank you for your support of our work,
Milton Friesen and the Social Cities Team
What is Milton Reading?
"Streampunks: YouTube and the Rebels Remaking Media" by Robert Kyncl.
This is a good book for Social Cities people to consider - myself included. The value of user-driven media platforms is the potential to adjust what you offer to the core "fans" in your network whether those fans are all about your DIY knitting projects, the cool music you write and produce, or, in the case of Social Cities, the content and ideas that focus on the people side of our urban environments. Growing this sensitivity will be a 2018 focus for Social Cities.
"St. Francis of Assisi" by G. K. Chesterton.
With a published date of 1924, this isn't exactly hot off the presses. Don't be alarmed. New isn't always better and Chesterton is worth a read even for people of no particular religious faith. St. Francis began a significant new social movement that arises, by Chesterton's analysis, from deep and unwavering love. St. Francis may seem odd, but only as odd as anyone in love is. That's the Chesterton insight that is worth considering. Social movements that matter have something to do with deep love.
"Statistical Modeling: The Two Cultures" by Leo Breiman.
Although an academic paper, not a book, you will be well-repaid in taking time to think through Breiman's arguments. Published in 2001 in Statistical Science (hey, wait, come back...), Breiman points out the emergence of two very different schools of thought arising from how we use statistics to interpret data. Typical regression is based on assumptions about normally distributed data and occupies 98% of statisticians (by Breiman's account). The other is an algorithm based, machine learning approach that doesn't assume normal distributions or clean data and is practiced by 2% of statisticians. Skip the relatively few numbers sections and read the text. Really, it will be worth it. Data scientists are becoming powerful story-tellers. It may be worth knowing what they are saying and why.
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