The beginning of the end?

Is this the beginning of the end, or only the end of the beginning as science absorbs itself up the vortex of modelling instead of observation and substitutes advocacy for criticism?
The following sets of quotes from recent articles do set one to pondering!

(Now, I know that plenty of good science is done, and published in places like Science and Nature; although…maybe not:

“A 2004 paper in Nature using the species-area model to predict species distribution in response to modelled climate change (in turn based upon emissions scenarios) concluded its abstract with a call to action: ‘These estimates show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.’ The paper itself presented neither reason nor evidence for such conclusions….”

and

“Science, for example, not only published the fraudulent research on cloning of Dr Woo Suk Hwang, but rushed it into print after short review so that it appeared in an electronic version, accompanied by a press release that ensured media coverage, on the eve of a key vote in the US Congress to overturn an administrative order of the Bush Administration prohibiting the use of federal funds for cloning research. Not only did it seem such research was more promising than was the case at that time, but South Korea was seemingly passing the US by.”)

Few scientists seem to heed the warning: “In science, the best kind of quality assurance is to celebrate sceptical dissent and to reject any attempt to tell us that we should bow to a consensus, that ‘the science is settled’ on principle— not just even, but especially when it supports our preferences. Because as Carl Sagan once put it, ‘Where we have strong emotions, we’re liable to fool ourselves.’ ” Oh, how so true on the question of origins!

(all quotes from Kellow, see below)
Then, there’s Ross Gittins, an economic commentator, in a recent Sydney Morning Herald article:

“The more important thing to say, however, is that whenever you hear the word "modelling", your bulldust detector should go into overdrive. In the debate about the Rudd Government's carbon pollution reduction scheme, I've been amused to see the Opposition and various business groups clamouring to see "the Treasury modelling". “How can we know what the scheme involves before we see the modelling?” they ask.
A better question would be, what makes you think seeing the modelling results will make you any the wiser? To me, demanding to see modelling is akin to crying, "con me, baby, con me". Blind me with science.
The simple truth is that human behaviour is extraordinarily hard to predict. And the modellers' use of computers and sets of fancy equations doesn't change that fact. Don't forget the sterling efforts of those modellers who predicted how many motorists would use the Cross City and Lane Cove tunnels.
…
Before you take much notice of any modelling results, you have to know all about the many hidden assumptions that have been made to produce them, and which particular variables are doing most to drive them. In the case of economic models, you have to decide whether you agree with the particular economic theory on which they're based

From his speech “All in a good cause: Framing science for public policy” by
Professor Aynsley Kellow

“The history of science is replete with error and fraud. Environmental science is no exception. Indeed, this area of science provides a hyperabundance of examples, thanks to the presence of two factors: a good cause and extensive reliance upon modelling, especially that involving sophisticated computer models.

“Modelling for the Bald Hills wind farm on the Orange- bellied Parrot assumed the birds spent time at most of the sites of wind farms in Victoria, despite the fact that the birds had not been recorded at 20 of the 23 sites along the coast of Victoria, and despite active searches having been conducted. Only one or two sightings had been made at the other three sites.

“The authors then assumed that the birds would remain present within a single wind farm location for six months—the longest possible period the migratory species could remain at a winter site, and longer than any bird had been recorded at any site. They also assumed the parrot would make two passes through the Bald Hills site. They did all this to err on the side of caution.

“So, while no parrot had been sighted within 50 kilometres of the proposed site, the minister then acted in accordance with the precautionary principle (and an election promise) to block Bald Hills on the basis of cumulative impact—compounding the precaution already embedded in the assumptions underlying the modeling.

“I have proposed in what I call Kellow’s Law that sightings of endangered species are clustered around the sites of proposed developments. This reflects not just the cynical uses of endangered species for political purposes, but partly also the fact that research for environmental assessments frequently finds species because the site has never previously been surveyed.

“Computer models fed by scenarios based on economic models are the norm in climate science, and when we are dealing with climate impacts on biodiversity, we are often dealing with species-area modelling fed by the modelled results of the impact of climate models on vegetation. It is important to understand the way in which the revolution in information technology has transformed the conduct of science. Its impact has come not just in the ability to model complex phenomena of which scientists a decade or so ago could only dream—though that is part of the problem. Computer models are always subject to the Garbage In – Garbage Out problem and they can never be a substitute for hypotheses tested against the cold, hard light of observational data.

“Many of the scientists working with models appear to have forgotten that science is about testing predictions against data. They seem to have fallen victim to the trap long-recognised at IBM, where it used to be said that simulation was like self-stimulation: if one practised it too often, one began to confuse it for the real thing.

“One problem with observational data in areas like climate science is that they themselves are subject to substantial massaging by computers before they are of any use. Even data collection, therefore, provides opportunities for subjective assumptions to intrude into the adjustments made to data to make them useful.