Community seeding

I suggested an idea to a doctor I know.  “No way anyone I know would do that”, she said.  She went on to explain that people going into medicine wanted to make decent money, and their insurance was so high they had to make a lot of money, and the only way to do that was to go to urban and regional areas.  But, I asked, what if?

What if insurance companies, in programs under-written by the government, agreed to create special rates to cover doctors and nurses who agree to open small local clinics in rural areas?

Think about how the country functioned 150 years ago.  There are so many small towns, especially those in the Midwest and West (that are now dying).  Some have tried financial incentives to get people to move to these places, and it did not work out so well because there was no connected, concentrated effort to “seed” the community with the services and professionals that serve as the infrastructure first.

Think of teachers: what if student loans were forgiven if professionals spend a certain number of years providing such infrastructure services in small towns?  How about schools that have multi-year classrooms?

Think about who makes the basic infrastructure in a community and make it bloom:  Electricians.  Plumbers.  Doctors, nurses, teachers, a lawyer or two, then shop keepers, maintenance persons of all types.  Seed the community with infrastructure first, and invite creative small businesses to open next (think organic farming, small scale grass fed animals, etc.).  You’ll find, I think, that people will be drawn to such small communities very quickly.

The sprawling suburbs with insane commutes are in decay.  Inner cities are experiencing gentrification, but only by those with the money that can afford to do so.  There are many young people who see the future and think there will be no place for them but starting groups who choose to seed communities together, it might work on so many levels.  Take this to your think tanks and see what happens. . .

Out-of-context parent quotes

(Note:  I’ll keep adding to this blog entry as time passes)

“Boys, the chicken is not a pillow to beat each other with”

“What about don’t-stick-your-finger-in-that do you not understand?”

“I’m sorry, I don’t know where his head is.  Look under the couch.”

“Why do you need an entire roll of tape to hang that up?”

“Why are there rocks in the bathtub?”

“What is that?”, “How did that get in here?”, “When did you do this?”, “Why did you do this?”

“No.  No.  No.  No. . .No.  I am not discussing Japanese theater, I said NO!”



I was listening to Kate Bush today and thumbing through my approximately 30 gig of music (and for the record, I wish I had more).  I remember when ITunes first came out with the data analyzer that attempts to make recommendations based on your library.  It was doomed to fail in so many ways (one because I have enough items that ITunes does not carry that it would be hard to adequately analyze if the case- my collection- has so much data that has to be ignored as unknown), but the errors are an interesting problem.   I have felt like much of my life I have been dismissed as an outlier.  In statistics, it is a convenient way to discard data by saying, in essence, “it does not fit anything we can use and is so far out of our margins of experience we can just throw it out”.

In data mining, eclectic cases could be interesting- but because they do not neatly provide predictive lines of analysis, they are more often than not dismissed as noise in the data.  Think of smoke coming out of your toaster (which is how I thought of ITunes when it tried to send me recommendations.  It was frying itself a little trying to make matches.  And yes, this is a vague reference to flying toasters.  And no, not from BSG).   “Noise” or errors in the data can also be used to refine pre-existing category systems (think when Photoshop tries to delete an object then insert surrounding inferential data to fill in where the object was removed), so that the greater the possibilities of realization of a concept is explored in real experience, it adds to the complexity of how that idea is understood and manifested, or reflected back.

On Facebook, I tried to limit the amount of information that could be freely collected by the site and its “partners”.  One of the things I did was put in a wildly inaccurate birth date.  I like messing with data mining.  I purposely have a small friends group too, so anyone who knows me well already knows my birthday and knows my penchant for messing with freeloading data systems.

When one knows oneself, we know the parts that are ridiculously stereotypical and the parts that are eclectic.  The mash up and resulting paradoxes are, I think, just part of being human.  Go figure that one out cognitive science- ok, I know people are trying but really- so many current models are so inadequate or just rehashing problems philosophers and early psychologists have better articulated.

There is something to the notions of innovation, eclecticism, and creativity that is compelling.  I formally studied these ideas long and hard for many years, for altruistic reasons as well as knowing I was a thorny case for the subject.   I found satisfying models and metaphors in work from people like R. Sternberg (the concept of Practical Intelligence), from the bulk of expertise research, the personal musings of many artists, musicians, and writers, aesthetics, and many other sources.   At the same time, there is an element of the wisp of smoke coming from a toaster about the idea of creativity; to try to understand open ended, eclectic thinking is to sometimes burn out the very tools of analysis you are bringing to the subject.

On a practical level, I have enjoyed the research that quite definitely shows if we do not use our brain in challenging and novel ways it will atrophy, and contribute to dementia.  As often as I feel like a complete alien in every culture and subculture I have been in, it is comforting to know my “sideways” way of viewing things may be helping me age better.  But does that also mean we are doomed to always be incomplete, constant learning beings if we are to survive, and survive well- thereby possibly both limiting our usefulness (incomplete, exploratory) as well as making us adaptable?  Ah, to be or not to be (apologies to WS).

We romanticize creativity, but in practice most folks are terrified of things that are different, and skeptical of the new.  I am not an early adopter of technology (one because I can’t afford to be), but I am deeply interested in what objects and processes new technology is applied to. I have been called blissfully naive in my life, and I took it as a put down.  But looking back, it was a habit of not making rash judgments, extending the time to understand someone or something (and yes, therefore putting myself in harms way from time to time) that garnered me that label, combined with a seemingly insatiable thirst for new experiences and stimuli.  I also know that withholding indexing or categorizing, being flexible in how one views an experience, is a core component of creativity.  It is the open-ended question without any one absolutely right answer that fascinates.  Wisdom for folks like me is learning what situations require immediate categorization and what situations can be allowed the extra time and thoughtfulness necessary for satisfactory input.

I don’t think data mining systems have reached the wisdom stage yet, and therefore will continue to emit wisps of smoke when confronted with the eclectic cases; or take so much of what I am and toss it out as outlier that what is left is a pale imitation, a grossly inadequate summary.  I think data mining (and life in general) does this to an extent to all of us, regardless of how predictable we may seem.  Some assumptions that result may be useful and help improve the tools that operate in our lives, both foregrounded and backgrounded.  Others lead to horrible policy and situations like those at airports when checking in for a flight.  In essence, everyone and no one are terrorists.  A complete paradox of institutionalized applications resulting from awful data analysis systems.

Simply using “that” word in this blog selects me out for further analysis in the gross internet search and filter systems of some intelligence programs.  Try and mash up all the topics I have crossed here and see if I am a no-fly—wisps of smoke, then throwing out most of what is written and determine I am not a threat due to inconsequential outliers.  See?  It is useful, right?

If only rejection were always so useful.

Husband recently said with a laugh, “I know WHO you are (and he trusts what he knows), but I don’t know WHAT you are.”   He struggled to explain- and remember, this man is an accomplished artist- that he meant he does not know where I fit in.  Funny, I don’t either most of the time.  But I’ll keep taking those wisps of smoke and the subsequent collapse into absurdity and laughter as the only way other than despair and insanity to synthesize the issue.  In laymans terms, come to a temporary peace.  But as many of us know, it is a peace that will soon be disturbed by the enjoyment of the knotty problem, the hurtful surprise, or the macro-level existential paradox that never ceases to exist in the back of every conscious mind.

So good night all you toasters, running your programs.  Maybe someday that AI will learn how to integrate the errors that lead to melt down or burnt toast; or maybe there will just not seem to be a good reason to mirror us and you’ll leave us alone.