WithoutPrintVille
First....
Hit the button on the Joe Jackson and then, while listening, read about the good stuff below (and click through on the links too)...
Sean Holman, who is shifting from hard news to (gasp!) analysis on the other side of the Rockies, has some BC-centric stuff up in his weekly round-up this weekend:
...In British Columbia, the government must disclose information that is in the public interest — regardless of any protections those records enjoy under the province’s freedom of information law. But, according to a investigation by information and privacy commissioner Elizabeth Denham, that requirement “has never been applied by a public body.” That’s because the information must also be of an “urgent” nature. As a result, Denham is calling for a legal change that would require “public bodies to disclose information of a non-urgent nature where disclosure is clearly in the public interest.”...
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After a visit to Tegucigalpa, Jody Paterson tells us why revolutions happen and how they can be very easily be prevented:
...Just how much wealth Honduras actually has is never clearer than when you're in Tegucigalpa, where the malls just keep getting bigger and the prices in the high-end designer stores are the same as what you'd find in the same store in New York City.
The contrast is disconcerting. In the capital, you could be dining at a super-flash Thai restaurant in Tegucigalpa listening to a fine jazz trio (see my little video above) even while the 14 kids at Angelitos back in Copan Ruinas are scratching by on the simplest diet imaginable in a children's home that regularly has neither electricity nor water because the woman who runs it can't afford to pay the bills.
I really hope the campesinos that my organization works with never have to see just how rich Tegus is, because the one saving grace about being poor in Honduras is knowing that so many others are poor too that it's almost a normal state. I fear it just might break their hearts to see for themselves how unbelievably wealthy some of their countrymen are, including their political leaders.
Wealth distribution ought to be a subject that consumes all of us. The gap between the rich and poor is tied to every health indicator out there, and is a significant determinant of the future of a country. If Honduras just took two per cent of the earnings of the top fifth and redistributed that money to the poorest fifth - as education scholarships, for instance - it would effectively increase their income by 40 per cent...
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In real life, I spend a good chunk of my time coming up with ways to test the function of individual genes (especially in their mutated forms) for people that pull them, like needles, out of fields full of massive genetic haystacks. So I am always a little flummoxed by the opaque wizardry that goes into the harvesting of those flowing fields of 'big data'. Turns out though, according to Aunt Pythia anyway, that what goes on there, at least at the front end before the algorithms are applied, isn't really so different than what I do:
...I (first) spend a lot of time with smallish samples getting the feel of things through “exploratory data analysis.” This helps make sure the data is clean, gives me the overall distribution and feel for the various data sources, and gives me some idea of the kind of relationships I might expect between the inputs and possibly the target, if there’s a well-defined target.
You’d be surprised how much you learn by doing that.
Next, how do you even choose which algorithm to use, never mind how exactly to tune the hyperparameters of a given algorithm? The answer is that it’s a craft, and over time you gain intuition, but at first you just don’t know and you experiment. Put the science in data science. Try a bunch of different ones and see which works better, and hypothesize on why, and try to test that hypothesis...
Ahhhhh....Hypotheses and all that. Now I get it!.
...I (first) spend a lot of time with smallish samples getting the feel of things through “exploratory data analysis.” This helps make sure the data is clean, gives me the overall distribution and feel for the various data sources, and gives me some idea of the kind of relationships I might expect between the inputs and possibly the target, if there’s a well-defined target.
You’d be surprised how much you learn by doing that.
Next, how do you even choose which algorithm to use, never mind how exactly to tune the hyperparameters of a given algorithm? The answer is that it’s a craft, and over time you gain intuition, but at first you just don’t know and you experiment. Put the science in data science. Try a bunch of different ones and see which works better, and hypothesize on why, and try to test that hypothesis...
Ahhhhh....Hypotheses and all that. Now I get it!.
And while we're on the subject (kinda/sorta), PZ Myers goes all contrarian on Richard Dawkinsey derrieres re: selfish-type genes and more/bigger cell/development contextian stuff (you can skip this one if you don't want to hurt your brain a little but it's worth it, and much milder than a minor hangover, if you do).
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Not strictly a blog thing, but Michael Mann the Climate Science Guy wonders why Global News pulled a solid polar warming/ice melting story from it's website.
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Finally, Cathy has something that will make you very, very happy, indeed (as karen notes in the comments, watch the happy, smiling faces).
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Why no Snooklandian stuff this morning?...Because it's Sunday, and I want to keep my head clear and my blood pressure down for one day of seven at least.
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