The good ones tell you when they’re wrong

I’m very sympathetic to Jay Rosen’s critique of American , particular The View from Nowhere perspective which renders so much journalism writing sterile and context-free. 

There are some journalists out there who are a pleasure to read because they write in their own voice. They don’t shy away from the subjective nature of good reporting.  Instead, these journalists will actually interpret events they report on and present events within a wider context. And, when they get things wrong, they tell you. To wit:

  • Spencer Ackerman of Wired’s Danger Room admitting he was wrong in his earlier hagiographic coverage of David Petraeus.
  • David Weigel of Slate admitting he was wrong about the effect of Presidential debates on close races.
  • Felix Salmon of Reuters admitting he was wrong in his critique of the composition of the Goldman Sachs board of directors.

This is what real intellectual honesty looks like.

Line by line

Through iTunes University , I’m following along in the lectures of a Yale course on modern American literature, authors like Hemingway, Faulkner and Fitzgerald. The professor talks about three registers of analysis: the macro, middle, and micro registers. At the micro register, the focus of the analysis is on things like the role of sensory information such as smell or sound. At the middle register, the focus of the analysis is on how authors of the time would experiment with narrative structure, such as the non-linear approach that Faulkner uses in The Sound and the Fury. At the macro register, the focus is on the larger historical context of the books. It’s only at the micro-level that you can do analysis by examining individual sentences. And, yet, the only way an author can write a book is to generate it by indvidual sentences.

We also talk about software at different levels of analysis, such as architecture for the higher levels, design patterns for the middle level, and lines of code at the micro level. There’s long been a yearning to be able to create new software by working at a higher level of abstraction. In today’s jargon, this is known as model-driven-development, where some kind of high-level graphical or textual model is created, and then is ultimately transformed into code. And this approach has found success in certain niches, such as Simulink, LabView, and Yahoo! Pipes.

For most applications, I suspect that the only way to write the software will continue to be the same as the only way to write novels: line by line.

Publications trump ideology

Dylan Matthews interviews Sasha Issenberg, the author of “The Victory Lab”, which is about  how political campaigns are increasingly applying social science research techniques.

It turns out that Democrat campaigns tend to apply these techniques more than Republicans, unsurprisingly, since academic researchers with knowledge of these techniques tend to lean left. However, Matthews notes that a lot of the important research in this area was done during the campaign of Republican governor Rick Perry in 2006. And why is that? According to Issenberg:

The reason Perry developed that partnership is that he made them an unusual offer, which is that they could publish their work.

Via Kevin Drum.

Training is a dirty word

Two posts caught my eye this week. The first was Anil Dash’s The Blue Collar Coder, and the second was Greg Wilson’s Dark Matter, Public Health, and Scientific Computing. Anil wrote about high school students and Greg spoke about scientists, but ultimately they’re both about teaching computer skills to people without a formal background in computing. In other words, training.

In the hierarchy of academia, training is pretty firmly at the bottom. Education at least gets some lip service, being the primary mission of the university and all. But training is a base, vulgar activity. And it’s a real shame, because the problems that Anil and Greg are trying to address are important ones that need solving. Help will need to come from somewhere else.

Relative confidence in scientific theories

One of the challenges of dealing with climate change is that it’s difficult to communicate to the public how much confidence the scientific community has in a particular theory. Here’s a hypothesis: people have a better intuitive grasp of relative comparisons (A is bigger than B) than they do with absolutes (we are 90% confident that “A” is big).

Assuming this hypothesis is true, we could do a broad survey of scientists and use them to rank-order confidence in various scientific theories that the general public is familiar with. Possible examples of theories:

  • Plate tectonics
  • Childhood vaccinations cause autism
  • Germ theory of disease
  • Theory of relativity
  • Cigarette smoking cause lung cancer
  • Diets rich in saturated fats cause heart disease
  • AIDS is caused by HIV
  • The death penalty reduces violent crime
  • Evolution by natural selection
  • Exposure to electromagnetic radiation from high-voltage power lines cause cancer
  • Intelligence is inherited biologically
  • Government stimulus spending reduces unemployment in a recession

Assuming the survey produced a (relatively) stable rank-ordering across these theories, the end goal would be to communicate confidence in a scientific theory by saying: “Scientists are more confident in theory X than they are in theories Y,Z, but not as confident as they are in theories P,Q”.