Help crowd source better science communication methods

Recently, studies aimed at better quantifying and communicating the “consensus” on climate change have become more popular. To take advantage of the increasing monetary flow in this direction, and to advance the science even further, our institute, meaning me, have been designing a new research protocol. In the spirit of the “open science” movement, we/I thought it would be good to get some public feedback on potential flaws and possible improvements to this protocol. There are several advantages of this “crowd sourcing” approach to blog science, not the least of which is avoidance of placing a rather tacky “Tip Jar” icon on one’s home page.

What we want to know is what sort of message will really stick with people, make them think. New research has shown that communication methods are very important in this regard. For example, van der Linden et al. (2014) showed that “simple text” and pie charts are slightly superior to mixed metaphors involving doctors’ opinions regarding bridge failures. This is an important advancement that we want to build upon, leveraging the moment to effectively effect an optimal messaging paradigm that cannot be falsified.

One improvement that can be made involves how the experimental units are chosen. van der Linden et al. (2014) queried about 1000 volunteers, but these were chosen from among a “nationwide panel of people who are willing to participate in online surveys”. Those people want to be asked random questions by unknown people having unknown motives, which being abnormal, is not representative of the entire population. A better approach is to just target everybody, and a good way to do that is to confront them on the street while they are minding their own business, before they have any idea what you’re up to really.

Another issue is the treatments themselves; van der Linden et al. used a set of treatments involving pie charts, metaphors and numbers. This is nice but c’mon we put a man on the moon; we believe we can achieve more here. Our design applies less pedestrian treatments to these pedestrian experimental units, each chosen after careful thought. Our procedure is similar however. That is, we first ask what each unit believes that scientists believe about the climate, record the response, then apply the randomly chosen treatment, repeat the original question, and record the second response. Pretty simple really; the whole thing hinges on the treatments, which are:

Treatment 1:
The unit is shown a pie chart with the AAAS logo below it, indicating that 97 percent of scientists believe that climate change is real.

Treatment 2:
The unit is shown a pie chart with images of kittens and Jesus below, with statement as above.

Treatment 3:
The unit is shown a rerun of an old Sesame Street episode featuring the numbers 9 and 7, in sequence, over a backdrop picture of a hurricane.

Treatment 4:
The unit is informed that only Australian Aborigines and Death Row inmates are unaware that 97 of scientists believe that climate change is real.

Treatment 5:
Free dinner and beer at a nice local pub is promised to the unit for all answers over 95 regarding what percentage of scientists believe in climate change.

Treatment 6:
“97% consensus” and “Mother” are tatooed prominently on the unit’s right inner forearm.

Treatment 7:
The unit’s face is situated ~ 0.3 meters proximate to the front end of an OSHA-certified megaphone and unit is informed three times, at the “riot control” setting, that 97 percent of scientists believe in climate change.

Treatment 8:
Justin Verlander is placed approximately 60.5 feet from unit, facing, and delivers a ~97 mph fastball to unit’s upper left rib cage quadrant, while yelling “Get a real-time feeling for what 97 is all about partner”

Many more treatments than this are possible of course. For example we can certainly improve upon the “Indirectness Factor” (IF) one or more steps by asking people what they think other people think scientists believe about the climate, what they think they would think if exposed to a particular treatment, and so forth. There is a rich garden for potential studies following this path.

Thank you in advance for any contributions to the science that you may have, the world will be a better place for it. If you would like to donate $1000 or more that would be fine as well.

Recent advancements on the “consensus” science front

Real good news from the world of science, just last week. Science as we all know, is all about “pushing the envelope”, about stretching the frontiers of knowledge, about intrepid explorations right on that knife-edged ridge that typically divides brilliance from ignorance and ineptitude. Science–let’s cut to the chase here–is all about putting it all out there on the line, in the quest for deep truths that affect us all.

Just last week, Climatic Change pushed on that envelope big time, with a fabulous discovery. A team of four researchers have discovered that, in situations where you’re having trouble getting people to buy in on a supposed “consensus” on some topic, such as say the 97 percent consensus regarding human effects on climate change, what you want to do there is to use either “simple text” or a “pie chart”. For the unfamiliar, pie charts are round, graphical devices in which a portion, p, of the round image is shaded one color and the remaining portion, 1-p, is shaded a different color altogether. [For sake of simplicity I have limited our hypothetical chart to two colors; advanced pie charts will sometimes use more than two colors, but we can simplify here without loss of generality]. “Simple text” is just what it says, sometimes even simpler.

When the human eye/brain/sensory system views said chart, an impression in the mind is created in which the two (or more) color shadings approximate actual fractional values of 1.0. Some refer to this as the theoretical/neurological basis of the pie chart. [Others do not; there is no consensus on that issue]. The point is, the pie chart can approximate an actual number!** This makes all the difference when trying to get a point across to the random ignoramus on the street.

I should caution the amateur scientists out there to please not try this at home. This type of research involves heavy duty online questioning* following, strict survey science guidelines, as informed by “metaphor meta-reviews for optimal persuasiveness”. It can involve the random insertion of questions involving “Angelina Jolie’s double mastectomy” so as not to divulge one’s true intentions. Not divulging one’s true intentions is a highly refined skill in consensus science–not just anybody can do it. This stuff takes training.

Well we’re out of time now but we can probably expect many future breakthroughs in the exciting world of “consensus” science studies as they relate to climate, and hopefully can investigate these as they occur, should we have the necessary chops and patience.

* The authors note: “All treatments contained the following message; ’97% of climate scientists have concluded that human-caused climate change is happening’. To enhance the credibility of the treatment, the logo of the American Association for the Advancement of Science (AAAS) was visible on every message.”. Not that the credibility of the “treatment” needs enhancing mind you, nor that AAAS partially funding the study has any relevance here; let’s not jump to any conclusions.

** This process can be enhanced by what professional pie chart communicators term “overlaying” the actual numerical number right on the pie chart itself, viz:
vanderlinden etal SM figure 2

On the Boulevard of Broken Dreams

Well now the gravity of trouble was more than I could bear
At times my luck was so bad, I had to fold my hand
I almost lost my soul, rarely could I find my head
Wake up early in the morning, feeling nearly dead

When I think of the old days, it sends chills up and down my spine
Life ain’t what it seems, on the Boulevard of Broken Dreams
I guess I opened my eyes in the nick of time
But it sure felt like the end of the line
Allman Brothers, End of the Line

Mail order mystics

Can’t you just go out and see the stars at night
Without asking someone “lucky” how to “see” them right?
You know these mail order mystics–they never had it so good
You go ahead, you listen like I knew you would
Hours of conversation–all about your “sign”
When all I want to talk about is love and wine

Don’t tell me death is easy when I’m dyin’ so slow
Go tell that to your doctor, it’s what you pay him for–
If he’s an old one he’ll be rollin’ round on the floor
It ain’t that I don’t hear, I just don’t listen no more
So you can go ahead, shoot yourself, I don’t mind
“Cause All I want to talk about is lovin’ and wine

Chris Smither, Mail Order Mystics

June US historical precipitation, a quick analysis

The NCDC June climate summary for the United States is out. June’s a real important month in the Northern Hemisphere, especially agriculturally. I’ll use these data as a chance to demonstrate the probability that June 2014 precipitation (P) in the US is drawn from a stationary distribution (over 1895-2014, 120 years, CONUS), using both state- and region-based P rankings, and 250k Monte Carlo simulations. [There are 97 years of record for Alaska, and Hawaii's not included.] If precipitation is stationary over that time, we expect the state rankings (normalized to a 0-1 scale) not to differ significantly from 0.5. Easier still, although a slightly different question, is to evaluate the probability of having 9 states rank among their 10 wettest (as was observed), as then we only need that information, not the actual rankings of each state.

Below are the graphics showing the rankings (120 = wettest; Alaska, not shown, had the 2nd wettest June in its 97 year record):
June 2014 CONUS state-based P rankings
June 2014 CONUS region-based P rankings

One nice thing about this is that only a few lines of code are needed. Here it is for the first situation:

## 1. State-based. Probability that 9 (of 49) states' June 2014 precip ranks among the state's wettest.  Hawaii not included.
# Non-Alaska: 120 years in the NCDC record (1895-2014).  Alaska: 97 year record (1918-2014)
# 1=dryest, 120=wettest; all rankings normalized to 0-1 scale; test stat = 111/120 = .9175. 

rm(list=ls()); options(digits=3)
trials=250000; data=rep(NA,trials)
for (i in 1:trials) {z1=sample(seq(.001,1,.001),size=49,replace=F); data[i]=length(z1[z1 >= 111/120])}
(p = length(data[data>=9])/trials)

States are not all the same size, so we should normalize accordingly. A quicker approximation is just to use climate regions, which are more roughly equal in size than the states are. However, there are only ten of them, so it might be better to look at their central tendency and dispersion, rather than the number placing in the ten wettest years. [Of course, for both analyses, it would be even better to use the actual P values themselves, instead of their ranks, but with 120 years of data, this will be a good approximation of that].

## 2. Region-based. Probability that mean and std. dev. of regional (including AK) June 2014 precip ranks exceed expectation under hypothesis of stationarity (no change).  Hawaii not included.
regn.ranks = c(c(88,40,107,120,112,97,23,13,50)/120, 96/97)
par1=mean(regn.ranks); par2=sd(regn.ranks)
trials=250000; data=matrix(NA,nrow=trials,ncol=2)
for (i in 1:trials){
 z1=sample((1:1000)/1000,10,F)
 data[i,1]=mean(z1); data[i,2]=sd(z1)
 print(i)
}
p.mean = length(data[,1][data[,1]>=par1])/trials
p.sd = length(data[,2][data[,2]>=par2])/trials

OK, so the results then. The state-based analysis (top) returns a value of p = 0.009, or just under 1%, for the probability of having 9 states out of 49 rank in the top 10 of their historical records. The region-based analysis gives p = 0.063 for a stationary mean, and p = 0.098 for a stationary standard deviation, at the region level, thus neither quite reaching the standard p = .05 signficance level, but both getting there. Remember, p = 0.5 would be the expected value for each metric under a stationary June precipitation; values deviating therefrom, either way, indicating evidence for dynamics. Note also that this is not a trend analysis; for that you would need the time series of either the P values or the rankings for each state or region.

The mountaineer’s privelege

There are turning-points in all men’s lives which must give them both pause and retrospect. In long Sierra journeys the mountaineer looks forward eagerly, gladly, till pass or ridge-crest is gained, and then, turning with a fonder interest, surveys the scene of his march; letting the eye wander over each crag and valley, every blue hollow of pine-land or sunlit gem of alpine meadow; discerning perchance some gentle reminder of himself in yon thin blue curl of smoke floating dimly upward from the smouldering embers of his last camp-fire. With a lingering look he starts forward, and the closing pass-gate with its granite walls shuts away the retrospect, yet the delightful picture forever after hangs on the gallery wall of his memory. It is thus with me about mountaineering; the pass which divides youth from man-hood is traversed, and the serious service of science must hereafter claim me. But as the cherished memories of Sierra climbs go ever with me, I may not lack the inspiring presence of sunlit snow nor the calming influence of those broad noble views. It is the mountaineer’s privilege to carry through life this wealth of unfading treasure. At his summons the white peaks loom above him as of old; the camp-fire burns once more for him, his study walls recede in twilight revery, and around him are gathered again stately columns of pine.

Clarence King, Preface to the Fourth Edition
Mountaineering in the Sierra Nevada

“Our eyes often ranged upward”

At last, after climbing a long, weary ascent, we rode out of the dazzling light of the foot-hills into a region of dense woodland, the road winding through avenues of pines so tall that the late evening light only came down to us in scattered rays. Under the deep shade of these trees we found an air pure and gratefully cool. Passing from the glare of the open country into the dusky forest, one seems to enter a door and ride into a vast covered hall. The whole sensation is of being roofed and enclosed. You are never tired of gazing down long vistas, where, in stately groups, stand tall shafts of pine. Columns they are, each with its own characteristic tinting and finish, yet all standing together with the air of relationship and harmony. Feathery branches, trimmed with living green, wave through the upper air, opening broken glimpses of the far blue, and catching on their polished surfaces reflections of the sun. Broad streams of light pour in, gilding purple trunks and falling in bright pathways along an undulating floor. Here and there are wide, open spaces, around which the trees group themselves in majestic ranks.

Our eyes often ranged upward, the long shafts leading the vision up to green, lighted spires, and on to the clouds. All that is dark and cool and grave in color, the beauty of blue umbrageous distance, all the sudden brilliance of strong local lights tinted upon green boughs or red and fluted shafts, surround us in ever-changing combination as we ride along these winding roadways of the Sierra.

Clarence King,
Mountaineering in the Sierra Nevada

Ebola epidemiology data scraper

Note: current as of 7-22-2014, 0:00 UTC

I wanted to find certain statistics for the West African Ebolavirus (EBV) outbreak from its inception, e.g. short-term case and death rates, among others. But the sites where’d I’d expect to find such (WHO, CDC etc) didn’t have them. However, starting with the WHO July 1 Global Alert and Response (GAR) report, the WHO is including tables of new and cumulative cases and deaths, for each of the three countries in the outbreak. Wikipedia has a nice, but less detailed, table for all dates back to March 25.

So, I wrote an R script to scrape, compile, rearrange, and plot the cumulative data, for any country or all together, as taken from the two sources. Feel free to improve upon.

Here also is a graph of the raw data, and loess-fitted trend, in new case rates (cases per day since the previous WHO report), from March 25 to July 12. Note that I recomputed “New” cases as the difference from the previous report’s total, to see how that would compare with the “New” column in the WHO tables. They are not identical. Note also that reporting issues, especially in Sierra Leone, are partly responsible for extreme fluctuations in the data.
Ebola case recent rates2b

The updates should be quite frequent now and what happens over the next week or two is utterly critical to just how bad this thing gets. The key issue is that the disease has now entered two very large cities, which separates it from all previous Ebola and Marburg outbreaks.

Continue reading

Ebola references

This is an abbreviated list of accessible, Ebola-related articles. Some are specific to the current outbreak, others are more general, some traditional science articles and others good online articles. [There are also a couple of useful links in the article below.] There will likely be a large number of articles emerging, potentially very large, given the nature of the situation.


1. Scientific literature and reports:
CDC, 2014, Mortality and Morbidity Weekly Reports. Ebola Viral Disease Outbreak — West Africa.
Gatherer, 2014, JGV (in press). The 2014 Ebola virus disease outbreak in west Africa
Baize et al, 2014, NEJM. Emergence of Zaire Ebola Virus Disease in Guinea-Preliminary Report
Feldman et al, 2003, Nature Reviews–Immunology. Ebola virus: from discovery to vaccine.
Vanessa and Matthias, 2012, JGID. Infection Control During Filoviral Hemorrhagic Fever Outbreaks
Legrand et al., E&I, 2007. Understanding the dynamics of Ebola epidemics
Jones, 2014, JGH. Ebola, Emerging.

2. Web pages:
WHO, 2014. Ebola virus disease, West Africa – update 10 July 2014
WHO, 2014. EBV FAQ
Black, 2014, on-site reporting (doctor with DWB, on scene in Sierra Leone), In the Shadow of Ebola and The First 24 Hour Shift
Poon, 2014, WUNC Radio. Ebola 101: The Facts Behind A Frightening Virus.

Ebola update

The West Africa Ebola epidemic is not waning. The latest (July 6) numbers from the WHO show that about 5.6% of the total cases (including probable and suspect cases), and 4.6% of the deaths, originated just in the four days from July 3 to July 6, inclusive. The minimum mortality rates are 350/626 (56%) for confirmed cases, and 543/894 (61%) when also including probable and suspect cases. The epidemic appears to be moving out of Guinea, and into Liberia and Sierra Leone, based on the recently reported cases. The WHO Ebola FAQ is here if you want to learn more about it, but on the outbreak itself there appears to be a serious dearth of reliable information.

  Country  Date   Type New* Confirmed Probable Suspect Total
1  Guinea 07_08  Cases    0       294       96      18   408
2 Liberia 07_08  Cases   16        63       30      38   131
3    S.L. 07_08  Cases   34       269       34       2   305
4   Total 07_08  Cases   50       626      160      58   844
5  Guinea 07_08 Deaths    2       195       96      16   307
6 Liberia 07_08 Deaths    9        41       28      15    84
7    S.L. 07_08 Deaths   14       114       11       2   127
8   Total 07_08 Deaths   25       350      135      33   518
#  *New cases (reported since 07_03) are neither classified nor included in Total

Updated values as of 7-10-14:

  Country  Date   Type New* Confirmed Probable Suspect Total
1  Guinea 07_10  Cases    1       296       96      17   409
2 Liberia 07_10  Cases   11        70       32      40   142
3    S.L. 07_10  Cases   32       298       34       5   337
4   Total 07_10  Cases   44       664      162      62   888
5  Guinea 07_10 Deaths    2       197       96      16   309
6 Liberia 07_10 Deaths    4        44       28      16    88
7    S.L. 07_10 Deaths   15       127       11       4   142
8   Total 07_10 Deaths   21       368      135      36   539
#   *New cases (reported since 07_08) are neither classified nor included in Total

Update:
1. New articles, both good, on Doctors Without Borders and The Economist web sites.

2. The Wikipedia page on the topic contains a summary table of the weekly course of reported cases and deaths since March.

Global temperature change computations using transient sensitivity, part two

In a recent post I made some crude estimates of projected global mean surface temperature (GMST) change to year 2090, based on the estimates of transient climate response/sensitivity (TCR/TCS) values taken from the AR5. The numbers I got were at the very low end of the AR5 90% confidence intervals and I couldn’t understand why. As mentioned there, my working assumption was that my numbers were most likely the indefensible ones. And that does indeed appear to be exactly the case, and fortunately the discrepancy between my values and the AR5′s was not too hard to spot. So, this post is to explain the mistake I made and provide a little associated discussion.

The very short version is that I forgot to account for the temperature time delay(s) in the earth system whenever an increasing RF is imposed over a number of years. Stupid mistake, but good learning experience. As a quick review, I got these median and mean GMST values, using the means and medians respectively of the 52 TCR values given in AR5 WG1 (45 are from Chapter 9, Tables 9.5 and 9.6). I applied the standard RF linear pro-rating to account for differences in delta RF from a doubling, the latter being how TCR is defined. For total anthro (TA) forcings, using the midpoints of the AR5 time intervals, I got these numbers:

     Period   RCP dT.med dT.mean AR5.mid
1 1995-2090 rcp26  0.433   0.444     1.0
2 1995-2090 rcp45  1.145   1.176     1.8
3 1995-2090 rcp60  1.632   1.676     2.2
4 1995-2090 rcp85  2.726   2.799     3.7

Let’s call it ~2.75 deg C for the RCP 8.5 scenario, or almost a degree below the AR5 midpoint.

TCR is clearly defined–the GMST change expected from a 1% per year increase in RF CO2 (or an equivalent RF from all sources, CO2eq), once 2x RF CO2/CO2eq has been reached. A 1.0 percent increase always takes ~70 years to reach doubling [log(2, base=1.01)], so the time span–and hence the intensity–of the imposed RF change, is clearly defined. The longer it takes for a given increase in RF, the greater the T change by the end of the interval, relative to that at some future time beyond it (i.e., when equilibrium is reached). Due to ocean heating and large scale mixing, only some fraction of the RF-induced T effects can manifest in 70 years, which is why the Equilibrium Climate Senstivity (ECS) is always quite a bit higher than the TCR is. The AR5 reference time period midpoints (1995-2090, 95 years) give an interval just a little longer than the defined TCR 70 year period, not enough to matter hugely it would seem.

The lagged temperature increase in the system depends on several factors, but the AR4 (2007) best estimate was for an additional increase of 0.6 degrees C by year 2100. For the RCP 8.5 scenario, adding that value to 2.75 brings the estimate to 3.35, so now we’re definitely in the ball park just by that adjustment alone. But that 0.6 does not include any lagged response(s) that might originate in say the earlier decades of the 21st century but not be manifest until toward the end thereof. Trying to estimate that value requires a close look at the CMIP5 model outputs. I’ll look again in the AR5 for any such estimates (any help on the issue appreciated!), but it’s not hard at all to see that another 0.35 degrees, or more, could arise therefrom, giving a midpoint estimate of 3.7 degrees C, for the RCP 8.5.

The topics of time lags and slow feedbacks are very interesting ones IMO (in any field of science) and the fact that these are substantial in global T responses to rapid RF changes is of major importance. The fact that the mean/median TCR estimates declined between AR4 and AR5 is not necessarily a good thing, given that ECS estimates did not change much with them*. From a strict predictability perspective, forcing the system hard and fast is not a good thing, and you surely don’t want a big gap between TCR and ECS estimates, because it will tend to increase the difficulty of identifying and communicating cause and effect relationships. And since the scientific understanding on climate change and its attribution certainly affects policy, that’s not a good situation to be in.

* On reflection, one should also consider the fact that TCR estimates based on observational data have more confidence attached to them, than do ECS estimates from same, making the whole topic that much more intriguing.

Notes:
1. Edited several times for clarity.
2. The xkcd cartoon that spurred my original post is +/- unaffected by these considerations, i.e. still biased high, (but not by as much as I’d originally thought either). This is because that cartoon uses (presumably), the middle 20th century as its baseline point, and the lagged 0.6 degree C addition to the year 2100 T estimate, and whatever might be due to RF increases from 2000 to ~2030, are already +/- included in any estimate of T change from 1950 to 2100. Most GHG-induced warming is post-1950, when [CO2] was about 310 ppm.

Against the grain…

Scientific thinking, which is analytic and objective, goes against the grain of traditional human thinking, which is associative and subjective.
Alan Cromer, American physicist and educator

I am going to have a sign put up all over my plant, reading “There is no expedient to which a man will not resort to avoid the real labor of thinking.
Thomas Edison

…one result of unimaginative, mechanistic thinking was that societies eventually ceased to burn people at the stake for witchcraft.
George Sudarshan, Indian physicist

Refs:
Cromer, A., 1993. Uncommon Sense: The Heretical Nature of Science. Oxford University Press, New York.
Runes, D. D., 1948. The Diary and Sundry Observations of Thomas Alva Edison, p. 167. Philosophical Library, New York.
Sudarshan, G., 1998. Doubt and Certainty: The Celebrated Academy: Debates on Science, Mysticism, Reality, in General on the Knowable and Unknowable, Fourth Debates. Perseus Books, Reading, MA.

Yep, all once again taken from: Gaither, C.C. and Cavazos-Gaither, A.E., 2008, Gaither’s Dictionary of Scientific Quotations, Springer.

I’m having a blast reading this thing so bear with me.

Accidental truth

The scientific spirit is of more value than its products, and irrationally held truths may be more harmful than reasoned errors.
Thomas Henry Huxley.

Accidental truth of a conclusion is no compensation for erroneous deduction.
Arthur Eddington

The very truth, and the nature of things, though repudiated and ordered into exile, sneaked in again through the back door, to be received by me under an unwonted guise.
Johannes Kepler

Refs:
Huxley, T.H., 1904. The Coming of Age of “The Origin of Species” p. 229. Macmillan & Company Ltd. London.
Eddington, A.S., 1921. Space, Time and Gravitation: An Outline of the General Relativity Theory, p. 29. The University Press. Cambridge, England.
Donahue, W.H., 1992. New Astronomy Part IV, p. 575. The University Press, Cambridge, England.

All as cited in: Gaither, C.C. and Cavazos-Gaither, A.E., 2008, Gaither’s Dictionary of Scientific Quotations, Springer.

To arrive at the simplest truth…

…as Newton knew and practiced, requires years of contemplation. Not activity. Not reasoning. Not calculating. Not busy behavior of any kind. Not reading. Not talking. Not making an effort. Not thinking. Simply bearing in mind what it is one needs to know. And yet those with the courage to tread this path to real discovery are not only offered practically no guidance on how to do so, they are actively discouraged and have to set about it in secret, pretending meanwhile to be diligently engaged in the frantic diversions and to conform with the deadening personal opinions which are continually being thrust upon them.

George Spencer-Brown, English mathematician

Spencer-Brown, G. 1969. Laws of Form, Appendix I, p. 110. George Allen & Unwin Ltd., London, England. Quoted in: Gaither, C.C. and Cavazos-Gaither, A.E., 2008, Gaither’s Dictionary of Scientific Quotations, Springer.

Well, isn’t that interesting…

Today, Ed Hawkins and a number of co-authors published a critical comment, in Nature, on a paper that came out many months ago, by Mora et al. I read that paper when it came out but I’ve got to go back to it again, since I’ve forgotten it. And I haven’t gotten to Hawkins et al’s comment, or the reply to it, yet.

The point of this post is not the content of the original paper, nor Hawkins et al’s comment on it, but rather what I noticed this evening at the Yale “environment 360″ blog. To wit, there is an extensive interview with Mora on his paper. But that paper came out nine months ago. Well, I think to myself at first glance, this must be about the comment that Hawkins et al. published today, and Mora et al’s response to it. So…I glance through the article–nothing about Hawkins et al’s comment today, at all. Nothing. Just Mora giving his opinion about various things, some not connected to the paper really.

So, I’d like to know, did the Yale e360 blog contact Mora, or did Mora contact Yale e360, regarding doing this interview? And just how is it that it got published on the very day that Hawkins et al’s comment was published, but with no mention thereof? I don’t like the looks of this, regardless of the arguments in the original paper, or in Hawkins et al’s comment. This isn’t just another blog putting this up, this is a blog run by Yale University.