(Hat tip: Andrew Gelman.) Uri Simonsohn's work is an impressively successful example of what I'm also trying to do. Christopher Shea's article provides a good account of his work and motivations. I was especially struck by this:
[What] is driving Simonsohn? His fraud-busting has an almost existential flavor. “I couldn’t tolerate knowing something was fake and not doing something about it,” he told me. “Everything loses meaning. What’s the point of writing a paper, fighting very hard to get it published, going to conferences?”
Simonsohn stressed that there’s a world of difference between data techniques that generate false positives, and fraud, but he said some academic psychologists have, until recently, been dangerously indifferent to both. Outright fraud is probably rare. Data manipulation is undoubtedly more common—and surely extends to other subjects dependent on statistical study, including biomedicine. Worse, sloppy statistics are “like steroids in baseball”: Throughout the affected fields, researchers who are too intellectually honest to use these tricks will publish less, and may perish. Meanwhile, the less fastidious flourish.
I think this is exactly right. If we do not—and especially if we cannot—expose fraud and correct error when we discover it then "everything loses meaning". Moreover, if those who concoct "positive" results have better odds of success in academia than those who spot mistakes (and therefore also avoid them in their own research) then the academy truly is in peril.
There's also a good interview with Simonsohn here.