My Faults My Own

…willing to sacrifice something we don't have

for something we won't have, so somebody will someday.

IN WHICH Ross Rheingans-Yoo, a sometimes-poet and erstwhile student of Computer Science and Math, oc­cas­ion­al­ly writes on things of int­erest.

Reading Feed (last update: November 24)

A collection of things that I was glad I read. Views expressed by linked authors are chosen because I think they're interesting, not because I think they're correct, unless indicated otherwise.


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Blog: Marginal Revolution | The Republican Club — why is this painting interesting? — Tyler plays art critic; see also The Democratic Club, by the same artist.

Blog: Marginal Revolution | A Time to Fast — on calorie reduction strategies.


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Blog: Marginal Revolution | The best results on assortative mating and inequality I have seen — "Individuals face a large degree of uncertainty about their permanent wages early in their careers. If they marry early, as most individuals in the late 1960s did, this uncertainty leads to weak marital sorting along permanent wage. But when marriage is delayed, as in the late 1980s, the sorting

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January 16 Links: Technologies, Games, and Play

Yes, the Friday linkwrap is, in fact, going out on Friday. We're living in the future!

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The Harvard Political Review reports that a Chicago nonprofit is scraping Twitter to pass on complaints about food poisoning in restaurants to the Chicago Department of Public Health:

Foodborne Chicago depends on human judgment in addition to computerized predictions. First, the algorithm "surfaces tweets that are related to foodborne illnesses." Next, "a human classifier goes through those complaints that the machine classifies, [...determining] what is really about food poisoning and what may be other noise." The Foodborne team then tweets back at the likely cases, providing a link for users to file an official complaint. In short, computers deal with the massive quantity of Twitter data, and humans ensure the quality of the result. According to its website, between its launch on March 23, 2013 and November 10, 2014, the Foodborne algorithm flagged 3,594 tweets as potential food poisoning cases. Of these tweets, human coders have identified 419, roughly 12 percent, as

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