A researcher's response to people pointing out 150 errors in four of his papers

Thanks, it looks like this one was done after I was searching. This study doesn't really have the outcomes we need to measure though. There is no placebo group, they didn't check for measles diagnoses (by doctors who didn't know whether they were vaccinated), and they only measured antibody levels for 1.5 months after 1 vaccine dose in children 12-23 months old (ie before going to school when exposure usually occurs).

Unfortunately, I really don't think this study offers us anything about whether "vaccines work". You can see that the main concern is whether doctors are much more likely to diagnose measles when they do not believe a vaccination occurred, and whether introduction of the lab tests made the diagnostic definition more stringent:

“This was not a blind study, since the investigators knew which children had received measles vaccine. It seems probable that the occurrence of so much ‘measles-like’ illness in the vaccinated children was a reflexion of the difficulty in making a firm diagnosis of measles in the African child at one visit.”

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2134550/

“A likely reason for this is that the case may have been misdiagnosed as a non-specific viral illness. Measles has become relatively uncommon in Singapore with two decades of widespread measles vaccination, and especially after the second dose policy was implemented in 1998. Many primary care doctors may not even see a single case of measles in a year. This makes diagnosis more difficult.

[...]

As only approximately 7% of the clinically-diagnosed cases of measles reported locally turned out to be measles by laboratory testing, there is a need for laboratory confirmation of measles to avoid misidentification of cases and improve disease surveillance.(2)”

http://www.ncbi.nlm.nih.gov/pubmed/17609829

> Climate Science is a bunch of computer models

All of science is eventually a bunch of models.

> models [that] are not testable. When they're run and compared to what actually happened they're always off

You do realize these statements contradict one another, right?

Also, you're wrong. There have been plenty of successful predictions: https://johncarlosbaez.wordpress.com/2013/02/05/successful-p...

> Climate Scientists don't release their model's code

False.

Some publish code before/with their papers, some don't publish at all, and some publish code with a lag after publication (to clean up the code, tussle with legal about IP and/or political issues, etc. Fair IMO as long as the code is eventually published).

The major models, including the NASA one, are open source.

Google it.

> Name a single climate science model that predicted "the pause" starting in '98?

Predicting century-long trends and predicting 5 or 10-year variations within that trend are extremely different tasks.

It's certainly possible that we have models that are exceptionally accurate on the scale of 20 or 50 or 100 years but cannot make accurate predictions over the course of 5 or 10 years.

In fact, in a stochastic system, that's exactly what you would expect to be the case -- trends that are globally but not locally discernible.

More-over, opponents to the climate change thesis don't bother to provide their own models and predictions for falsification. And when they do, they're just as wrong on small time scales and much more wrong on long time scales.

> I'm saying that labeling dissenters as deniers (reminiscent of holocaust deniers) is beyond the pale.

This largely depends on what is being denied:

  - CO2 effects climate (requires extraordinary new evidence -- we have both historical Earth data and inter-planetary data).
  - Temperatures are changing (requires new evidence -- the '98 "hiatus" as largely played out and skeptics failed to make any predictions that made it through the decade).
  - CO2 is causing temperatures to change on earth (again, requires new evidence).
  - Concrete predictions about near and mid-term effects of climate change. IMO fair game, but I'm not sure what the point is from a climate policy perspective unless we're really that selfish.

There's also an important difference between skepticism and denial.

Being skeptical of Newtonian physics and providing a competing model against which we can compare is of course fair game.

Denying Newtonian physics in favor of "no particular opinion on how the world works, and let's not try to figure out" is of course worse than useless.

In other words, make your own predictions or get off the pot.

> Where's the problem? If that $1 million came from public sources, then the data should have been shared from day one.

I'm not saying the data should never be shared. Often times you can contact the author of a particular study and they will provide anonymized data. Most of the time people don't ask, and to take the time to prepare data for public release would offer little to no benefit.

I do believe there should be "first rights" to publish on data collected with funding from public sources. As for an exact policy by which it could be handled, I don't have one in mind. When you spend dozens of hours writing grant proposals only to have most of them turned down, it would be a sucky situation to have to make the data available for everyone before you have gotten a single paper out of it.

> If others use the data they should put the originators on the data as coauthors on the resulting papers.

It may vary by field, but generating the data alone is almost certainly not sufficient for co-authorship. [1]

[1] http://www.icmje.org/recommendations/browse/roles-and-respon...

   generating the data alone    

Manus manum lavat.

The closest the left has to mainstream anti-vax politicians are the Green Party. They got a couple percent of the vote in the last election.

On the right, virtually all mainstream politicians deny climate change.