1) Papers describe methods in enough detail to reproduce them. If they don't, there's a serious problem.
2) Independent lines of verification. If simulation code becomes a reference, it's inevitable that the same bugs/bad assumptions will contaminate an entire field. Independent re-implementation of the same algorithms is a strong hedge against this phenomenon (even if it means that there are more bugs overall).
3) Money. A lot of scientists fund their research in part through licensing of implementations of their algorithms. I don't like it, but until someone gets around to repealing Bayh-Dole (a real scientific travesty, IMO), this is going to continue to be a problem.
In short, what you really meant to say was that finally someone wrote a newspaper article about this subject. It's not a new discussion.
; Plots 24 yearly maps of calibrated (PCR-infilled or not) MXD reconstructions ; of growing season temperatures. Uses “corrected” MXD – but shouldn’t usually ; plot past 1960 because these will be artificially adjusted to look closer to ; the real temperatures.
Do you expect the average programmer to take a 2 year course in climate science to truly understand what MXD means instead of instantly running around screaming "wolf!"? If you did so, be disappointed (a careful reading of Google results for "CRU code" might be enlightening).
Other people read the CRU code and made intelligent comments about the code.
http://matthewmarkus.posterous.com/raw-darwin-airport-temper... http://matthewmarkus.posterous.com/darwin-airport-temperatur... http://matthewmarkus.posterous.com/darwin-airport-temperatur...
I found at least one error/discrepancy in each side's output. Of course, neither side released any code. The disclosure of methods is a prerequisite for repeatable experiments, the cornerstone of science.
Background experience: I refereed somewhat over 100 papers and perhaps a dozen or so books during my career as a physicist. My work now overlaps with the scientific publishing industry more broadly.
"Intellectually bankrupt" is a pretty strong term to use for people who work for a small fraction of the amount of money normally talked about on this site.
I'm not saying there aren't issues, but blaming the individuals who are trying to make a living by doing science isn't going to help. The success rate of getting permanent jobs in science might be higher than that of startups, but the "payoff" is a small fraction.
There are many of us who've left academia and still do science. We're generally maligned, and removed from the ability to even participate in a discussion due to a variety of academic access restrictions, and why?
What's more, day by day people are showing how to achieve scientific credibility and influence through their blogs and paper hosting services like ArXiv or, as Michael Nielsen points out, open journals like PLoS Biology. The majority of scientists still bow to tenure pressure, and frankly I don't understand why. There are other opportunities if you want to gain status, and one doesn't even have to gain traditional academic status if one wants to do real science. There are other options.
I'm not sure blogs are a relevant source for scientific studies though. Not necessarily because I think peer review is the greatest system, but having your paper published in an actual journal (open journals are fine) at least means you managed to convince a few other people that it's worth looking at the paper.
1) Code is notoriosly difficult to describe in a natural language, so it's unlikely papers contain enough detail to replicate a complex program. 2) People can still reimplement the model even when it's available. In fact it's going to be more likely that non-scientists join the convesation (which will improve the abysmall public perception of science, especially climate science). 3) Making money is great for an inventor and/or startup founder. You can still sell implementations, but they can't be trusted until they have been run on a large number of independent computers. Until then, it's not science.