Feeling the Research

Daryl Bem must be sick of those puns by now.

Back in 2011 he published Feeling the Future, a paper that combined multiple experiments on human precognition to argue it was a thing. Naturally this led to a flurry of replications, many of which riffed on his original title. I got interested via a series of blog posts I wrote that, rather surprisingly, used what he published to conclude precognition doesn’t exist.

I haven’t been Bem’s only critic, and one that’s a lot higher profile than I has extensively engaged with him both publicly and privately. In the process, they published Bem’s raw data. For months, I’ve wanted to revisit that series with this new bit of data, but I’m realising as I type this that it shouldn’t live in that Bayes 20x series. I don’t need to introduce any new statistical tools to do this analysis, for starters; all the new content here relates to the dataset itself. To make understanding that easier, I’ve taken the original Excel files and tossed them into a Google spreadsheet. I’ve re-organized the sheets in order of when the experiment was done, added some new columns for numeric analysis, and popped a few annotations in.

Odd Data

The first thing I noticed was that the experiments were not presented in the order they were actually conducted. It looks like he re-organized the studies to make a better narrative for the paper, implying he had a grand plan when in fact he was switching between experimental designs. This doesn’t affect the science, though, and while never stating the exact order Bem hints at this reordering on pages three and nine of Feeling the Future.

What may affect the science are the odd timings present within many of the datasets. As Dr. R pointed out in an earlier link, Bem combined two 50-sample studies together for the fifth experiment in his paper, and three studies of 91, 19, and 40 students for the sixth. Pasting together studies like that is a problem within frequentist statistics, due to the “stopping problem.” Stopping early is bad, because random fluctuations may blow the p-value across the “statistically significant” line when additional data would have revealed a non-significant result; but stopping too late is also bad, because p-values tend to exaggerate the evidence against the null hypothesis and the problem gets worse the more data you add.

But when pouring over the datasets, I noticed additional gaps and oddities that Dr. R missed. Each dataset has a timestamp for when subjects took the test, presumably generated by the hardware or software. These subjects were undergrad students at a college, and grad students likely administered some or all the tests. So we’d expect subject timestamps to be largely Monday to Friday affairs in a continuous block. Since these are machine generated or copy-pasted from machine-generated logs, we should see a monotonous increase.

Yet that 91 study which makes up part of the sixth study has a three-month gap after subject #50. Presumably the summer break prevented Bem from finding subjects, but what sort of study runs for a month, stops for three, then carries on for one more? On the other hand, that logic rules out all forms of replication. If the experimental parameters and procedure did not change over that time-span, either by the researcher’s hand or due to external events, there’s no reason to think the later subjects differ from the former.

Look more carefully and you see that up until subject #49 there were several subjects per day, followed by a near two-week pause until subject #50 arrived. It looks an awful like Bem was aiming for fifty subjects during that time, was content when he reached fourty-nine, then luck and/or a desire for even numbers made him add number fifty. If Bem was really aiming for at least 100 subjects, as he claimed in a footnote on page three of his paper, he could have easily added more than fifty, paused the study, and resumed in the fall semester. Most likely, he was aiming for a study of fifty subjects back then, suggesting the remaining forty-one were originally the start of a second study before later being merged.

Experiment 1, 2, 4, and 7 also show odd timestamps. Many of these can be explained by Spring Break or Thanksgiving holidays, but many also stop at round numbers. There’s also instances where some timestamps occur out-of-order or the sequence number reverses itself. This is pretty strong evidence of human tampering, though “tampering” isn’t the synonymous with “fraud;” any sufficiently large study will have mistakes, and any attempt to correct those mistakes will look like fraud. That still creates uncertainty in a dataset and necessarily lowers our trust in it.

I’ve also added stats for the individual runs, and some of them paint an interesting tale. Take experiment 2, for instance. As of the pause after subject #20, the success rate was 52.36%, but between subject #20 and #100 it was instead 51.04%. The remaining 50 subjects had a success rate of 52.39%, bringing the total rate up to 51.67%. Why did I place a division between those first hundred and last fifty? There’s no time-stamp gap there, and no sign of a parameter shift. Nonetheless, if we look at page five and six of the paper, we find:

For the first 100 sessions, the flashed positive and negative pictures were independently selected and sequenced randomly. For the subsequent 50 sessions, the negative pictures were put into a fixed sequence, ranging from those that had been successfully avoided most frequently during the first 100 sessions to those that had been avoided least frequently. If the participant selected the target, the positive picture was flashed subliminally as before, but the unexposed negative picture was retained for the next trial; if the participant selected the nontarget, the negative picture was flashed and the next positive and negative pictures in the queue were used for the next trial. In other words, no picture was exposed more than once, but a successfully avoided negative picture was retained over trials until it was eventually invoked by the participant and exposed subliminally. The working hypothesis behind this variation in the study was that the psi effect might be stronger if the most successfully avoided negative stimuli were used repeatedly until they were eventually invoked.

So precisely when Bem hit a round number and found the signal strength was getting weaker, he tweaked the parameters of the experiment? That’s sketchy, especially if he peeked at the data during the pause at subject #20. If he didn’t, the parameter tweak is easier to justify, as he’d already hit his goal of 100 subjects and had time left in the semester to experiment. Combining both experimental runs would still be a no-no, though.

Uncontrolled Controls

Bem’s inconsistent use of controls was present in the paper, but it’s a lot more obvious in the dataset. In experiments 2, 3, 4, and 7 there is no control group at all. That is dangerous. If you run a control group through a protocol nearly identical to that of the experimental group, and you don’t get a null result, you’ve got good evidence that the procedure is flawed. If you don’t run a control group, you’d better be damn sure your experimental procedure has been proven reliable in prior studies, and that you’re following the procedure close enough to prevent bias.

Bem doesn’t hit that for experiments 2 and 7; the latter isn’t the replication of a prior study he’s carried out, and while the former is a replication of experiment 1 the earlier study was carried out two years before and appears to have been two separate sample runs pasted together, each with different parameters. In experiments 3 and 4, Bem’s comparing something he knows will have an effect (forward priming) with something he hopes will have an effect (retroactive priming). There’s no explicit comparison of the known-effect’s size to that found in other studies, Bem’s write-up appears to settle for showing statistical significance. Merely showing there is an effect does not demonstrate that effect is of the same magnitude as expected.

Conversely, experiments 5 and 6 have a very large number of controls, relative to the experimental conditions. This is wasteful, certainly, but it could also throw off the analysis: since the confidence interval narrows as more samples are taken, we can tighten one side up by throwing more datapoints in and taking advantage of the p-value’s weakness.

Experiment 6 might show this in action. For the first fifty subjects, the control group was further from the null value than the negative image group, but not as extreme as the erotic image one. Three months later, the next fourty-one subjects are further from the null value than both the experimental groups, but this time in the opposite direction! Here, Bem drops the size of the experimental groups and increases the size of the control group; for the next nineteen subjects, the control group is again more extreme than the negative image group and again less extreme than the erotic group, plus the polarity has flipped again. For the last fourty subjects, Bem increased the sizes of all groups by 25%, but the control is again more extreme and the polarity has flipped yet once more. Nonetheless, adding all four runs together allows all that flopping to cancel out, and Bem to honestly write “On the neutral control trials, participants scored at chance level: 49.3%, t(149) = -0.66, p = .51, two-tailed.” This looks a lot like tweaking parameters on-the-fly to get a desired outcome.

It also shows there’s substantial noise in Bem’s instruments. What’s the odds that the negative image group success rate would show less variance than the control group, despite having anywhere from a third to a sixth of the sample size? How can their success rate show less variance than the erotic image group, despite having the same sample size? These scenarios aren’t impossible, but with them coming at a time when Bem was focused on precognition via negative images it’s all quite suspicious.

The Control Isn’t a Control

All too often, researchers using frequentist statistics get blinded by the way p-values ignore the null hypothesis, and don’t bother checking their control groups. Bem’s fairly good about this, but we can do better.

All of Bem’s experiments, save 3 and 4, rely on Bernoulli processes; every person has some probability of guessing the next binary choice correctly, due possibly to inherent precognitive ability, and that probability does not change with time. It follows that the distribution of successful guesses follows the binomial distribution, which can be written:

P( s `divides` p,f ) ~=~ { (s+f)"!" } over { s"!" f"!" } p^s ( 1-p )^f where s is the number of successes, f the number of failures, and p the odds of success; that means P ( s | p,f ) translates to “the probability of having s successes, given the odds of success are p and there were f failures.” Naturally, p must be between 0 and 1.

Let’s try a thought experiment: say you want to test if a single six-sided die is biased to come up 1. You roll it thirty-six times, and observe four instances where it comes up 1. Your friend tosses it seventy-two times, and spots fifteen instances of 1. You’d really like to pool your results together and get a better idea of how fair the die is; how would you do this? If you answered “just add all the successes together, as well as the failures,” you nailed it!The probability distribution of rolling a 1 for a given die, according to you and your friend's experiments.The results look pretty good; both you and your friend would have suspected the die was biased based on your individual rolls, but the combined distribution looks like what you’d expect from a fair die.

But my Bayes 208 post was on conjugate distributions, which defang a lot of the mathematical complexity that comes from Bayesian methods by allowing you to merge statistical distributions. Sit back and think about what just happened: both you and your friend examined the same Bernoulli process, resulting in two experiments and two different binomial distributions. When we combined both experiments, we got back another binomial distribution. The only way this differs from Bayesian conjugate distributions is the labeling; had I declared your binomial to be the prior, and your friend’s to be the likelihood, it’d be obvious the combination was the posterior distribution for the odds of rolling a 1.

Well, almost the only difference. Most sources don’t list the binomial distribution as the conjugate for this situation, but instead the Beta distribution:

Beta( p `divides` %alpha,%beta ) ~=~ { %GAMMA(%alpha + %beta) } over { %GAMMA(%alpha) %GAMMA(%beta) } p^{%alpha-1} ( 1-p )^{%beta-1}

But I think you can work out the two are almost identical, without any help from me. The only real advantage of the Beta distribution is that it allows non-integer successes and failures, thanks to the Gamma function, which in turn permits a nice selection of priors.

In theory, then, it’s dirt easy to do a Bayesian analysis of Bem’s handiwork: tally up the successes and failures from each individual experiment, add them together, and plunk them into a binomial distribution. In practice, there are three hurdles. The easy one is the choice of prior; fortunately, Bem’s datasets are large enough that they swamp any reasonable prior, so I’ll just use the Bayes-Laplace one and be done with it. A bigger one is that we’ve got at least three distinct Bernoulli processes in play: pressing a button to classify an image (experiments 3, 4), remembering a word from a list (8, 9), and guessing the next image out of a binary pair (everything else). If you’re trying to describe precognition and think it varies depending on the input image, then the negative image trials have to be separated from the erotic image ones. Still, this amounts to little more than being careful with the datasets and thinking hard about how a universal precognition would be expressed via those separate processes.

The toughest of the bunch: Bem didn’t record the number of successes and failures, save experiments 8 and 9. Instead, he either saved log timings (experiments 3 and 4) or the success rate, as a percentage of all trials. This is common within frequentist statistics, which is obsessed with maximal likelihoods, but it destroys information we could use to build a posterior distribution. Still, this omission isn’t fatal. We know the number of successes and failures are integer values. If we correctly guess their sum and multiply it by the rate, the result will be an integer; if we pick an incorrect sum, it’ll be a fraction. A complication arrives if there are common factors between the number of successes and the total trials, but there should some results which lack those factors. By comparing results to one another, we should be able to work out both what the underlying total was, as well as when that total changes, and in the process we learn the number of successes and can work backwards to the number of failures.

As the heading suggests, there’s something interesting hidden in the control groups. I’ll start with the binary image pair controls, which behave a lot like a coin flip; as the samples pile up, we’d expect the control distribution to migrate to the 50% line. When we do all the gathering, we find…

What happens when we combine the control groups for the binary image process from Bem (2011).… that’s not good. Experiment 1 had a great control group, but the controls from experiment 5 and 6 are oddly skewed. Since they had a lot more samples, they wind up dominating the posterior distribution and we find ourselves with fully 92.5% of the distribution below the expected value of p = 0.5. This sets up a bad precedent, because we now know that Bem’s methodology can create a skew of 0.67% away from 50%; for comparison, the combined signal from all studies was a skew of 0.83%. Are there bigger skews in the methodology of experiments 2, 3, 4, or 7? We’ve got no idea, because Bem never ran control groups.

Experiments 3 and 4 lack any sort of control, so we’re left to consider the strongest pair of experiments in Bem’s paper, 8 and 9. Bem used a Differential Recall score instead of the raw guess count, as it makes the null effect have an expected value of zero. This Bayesian analysis can cope with a non-zero null, so I’ll just use a conventional success/failure count.

Experiments 8 and 9 from Bem's 2011 paper.

On the surface, everything’s on the up-and-up. The controls have more datapoints between them than the treatment group, but there’s good and consistent separation between them and the treatment. Look very careful at the numbers on the bottom, though; the effects are in quite different places. That’s strange, given the second study only differs from the first via some extra practice (page 14); I can see that improving up the main control and treatment groups, but why does it also drag along the no-practice groups? Either there aren’t enough samples here to get rid of random noise, which seems unlikely, or the methodology changed enough to spoil the replication.

Come to think of it, one of those controls isn’t exactly a control. I’ll let Bem explain the difference.

Participants were first shown a set of words and given a free recall test of those words. They were then given a set of practice exercises on a randomly selected subset of those words. The psi hypothesis was that the practice exercises would retroactively facilitate the recall of those words, and, hence, participants would recall more of the to-be-practiced words than the unpracticed words. […]

Although no control group was needed to test the psi hypothesis in this experiment, we ran 25 control sessions in which the computer again randomly selected a 24-word practice set but did not actually administer the practice exercises. These control sessions were interspersed among the experimental sessions, and the experimenter was uninformed as to condition. [page 13]

So the “no-practice treatment,” as I dubbed it in the charts, is actually a test of precognition! It happens to be a lousy one, as without a round of post-hoc practice to prepare subjects their performance should be poor. Nonetheless, we’d expect it to be as good or better than the matching controls. So why, instead, was it consistently worse? And not just a little worse, either; for experiment 9, it was as worse from its control as the main control was from its treatment group.

What it all Means

I know, I seems to be a touch obsessed with one social science paper. The reason has less to do with the paper than the context around it: you can make a good argument that the current reproducibility crisis is thanks to Bem. Take the words of E.J. Wagenmakers et al.

Instead of revising our beliefs regarding psi, Bem’s research should instead cause us to revise our beliefs on methodology: The field of psychology currently uses methodological and statistical strategies that are too weak, too malleable, and offer far too many opportunities for researchers to befuddle themselves and their peers. […]

We realize that the above flaws are not unique to the experiments reported by Bem (2011). Indeed, many studies in experimental psychology suffer from the same mistakes. However, this state of affairs does not exonerate the Bem experiments. Instead, these experiments highlight the relative ease with which an inventive researcher can produce significant results even when the null hypothesis is true. This evidently poses a significant problem for the field and impedes progress on phenomena that are replicable and important.

Wagenmakers, Eric–Jan, et al. “Why psychologists must change the way they analyze their data: the case of psi: comment on Bem (2011).” (2011): 426.

When it was pointed out Bayesian methods wiped away his results, Bem started doing Bayesian analysis. When others pointed out a meta-analysis could do the same, Bem did that too. You want open data? Bem was a hipster on that front, sharing his data around to interested researchers and now the public. He’s been pushing for replication, too, and in recent years has begun pre-registering studies to stem the garden of forking paths. Bem appears to be following the rules of science, to the letter.

I also know from bitter experience that any sufficiently large research project will run into data quality issues. But, now that I’ve looked at Bem’s raw data, I’m feeling hoodwinked. I expected a few isolated issues, but nothing on this scale. If Bem’s 2011 paper really is a type specimen for what’s wrong with the scientific method, as practiced, then it implies that most scientists are garbage at designing experiments and collecting data.

I’m not sure I can accept that.

How to Become a Radical

If I had a word of the week, it would be “radicalization.” Some of why the term is hot in my circles is due to offline conversations, some of it stems from yet another aggrieved white male engaging in terrorism, and some from yet another study confirms Trump voters were driven by bigotry (via fearing the loss of privilege that comes from giving up your superiority to promote equality).

Some just came in via Rebecca Watson, though, who pointed me to a fascinating study.

For example, a shift from ‘I’ to ‘We’ was found to reflect a change from an individual to a collective identity (…). Social status is also related to the extent to which first person pronouns are used in communication. Low-status individuals use ‘I’ more than high-status individuals (…), while high-status individuals use ‘we’ more often (…). This pattern is observed both in real life and on Internet forums (…). Hence, a shift from “I” to “we” may signal an individual’s identification with the group and a rise in status when becoming an accepted member of the group.

… I think you can guess what Step Two is. Walk away from the screen, find a pen and paper, write down your guess, then read the next paragraph.

The forum investigated here is one of the largest Internet forums in Sweden, called Flashback (…). The forum claims to work for freedom of speech. It has over one million users who, in total, write 15 000 to 20 000 posts every day. It is often criticized for being extreme, for example in being too lenient regarding drug related posts but also for being hostile in allowing denigrating posts toward groups such as immigrants, Jews, Romas, and feminists. The forum has many sub-forums and we investigate one of these, which focuses on immigration issues.

The total text data from the sub-forum consists of 964 Megabytes. The total amount of data includes 700,000 posts from 11th of July, 2004 until 25th of April, 2015.

How did you do? I don’t think you’ll need pen or paper to guess what these scientists saw in Step Three.

We expected and found changes in cues related to group identity formation and intergroup differentiation. Specifically, there was a significant decrease in the use of ‘I’ and a simultaneous increase in the use of ‘we’ and ‘they’. This has previously been related to group identity formation and differentiation to one or more outgroups (…). Increased usage of plural, and decreased frequency of singular, nouns have also been found in both normal, and extremist, group formations (…). There was a decrease in singular pronouns and a relative increase in collective pronouns. The increase in collective pronouns referred both to the ingroup (we) and to one or more outgroups (they). These results suggest a shift toward a collective identity among participants, and a stronger differentiation between the own group and the outgroup(s).

Brilliant! We’ve confirmed one way people become radicalized: by hanging around in forums devoted to “free speech,” the hate dumped on certain groups gradually creates an in-group/out-group dichotomy, bringing out the worst in us.

Unfortunately, there’s a problem with the staircase.

Categories Dictionaries Example words Mean r
Group differentiation First person singular I, my, me -.0103 ***
First person plural We, our, us .0115 ***
Third person plural They, them, their .0081 ***
Certainty Absolutely, sure .0016 NS

***p < .001. NS = not significant. n=11,751.

Table 2 tripped me up, hard. I dropped by the ever-awesome R<-Psychologist and cooked up two versions of the same dataset. One has no correlation, while the other has a correlation coefficient of 0.01. Can you tell me which is which, without resorting to a straight-edge or photo editor?

Comparing two datasets, one with r=0, the other with r=0.01.

I can’t either, because the effect size is waaaaaay too small to be perceptible. That’s a problem, because it can be trivially easy to manufacture a bias at least that large. If we were talking about a system with very tight constraints on its behaviour, like the Higgs Boson, then uncovering 500 bits of evidence over 2,500,000,000,000,000,000 trials could be too much for any bias to manufacture. But this study involves linguistics, which is far less precise than the Standard Model, so I need a solid demonstration of why this study is immune to biases on the scale of r = 0.01.

The authors do try to correct for how p-values exaggerate the evidence in large samples, but they do it by plucking p < 0.001 out of a hat. Not good enough; how does that p-value relate to studies of similar subject matter and methodology? Also, p-values stink. Also also, I notice there’s no control sample here. Do pro-social justice groups exhibit the same trend over time? What about the comment section of sports articles? It’s great that their hypotheses were supported by the data, don’t get me wrong, but it would be better if they’d tried harder to swat down their own hypothesis. I’d also like to point out that none of my complaints falsify their hypotheses, they merely demonstrate that the study falls well short of confirmed or significant, contrary to what I typed earlier.

Alas, I’ve discovered another path towards radicalization: perform honest research about the epistemology behind science. It’ll ruin your ability to read scientific papers, and leave you in despair about the current state of science.

Bayes Bunny iz trying to cool off after reading too many scientific papers.

The Laziness of Steven Pinker

I know, I know, I should have promoted that OrbitCon talk on Steven Pinker before it aired. I was a bit swamped developing material for it, ironically, most of which never made it to air. Don’t worry, I’ll be sharing the good bits via blog post. Amusingly, this first example isn’t from that material. I wound up reading a lot of Pinker, and developed a hunch I wasn’t able to track down before air time. In a stroke of luck, Siggy handed me the material I needed to properly follow up.

Enough suspense: what’s your opinion of self-plagiarism, or copying your own work without flagging what you’ve done?

… self-plagiarism does carry with it some level of dishonesty, at least in some situations. The problem is that, when an author, artist or other creator presents a new work, it’s generally expected to be all-new content, unless otherwise clearly stated. … with an academic paper, one is generally expected to showcase what they have learned most recently, meaning that self-plagiarism defeats the purpose of the paper or the assignment. On the other hand, in a creative environment, however, reusing old passages, especially in a limited manner, might be more about homage and maintaining consistency than plagiarism.

It’s a bit of a gray area, isn’t it? The US Office of Research Integrity declares it unethical, but also declares that self-plagiarism isn’t misconduct. Nonetheless it could be considered misconduct in an academic context, and the ORI themselves outline the case:

For example, in one editorial, Schein (2001) describes the results of a study he and a colleague carried out which found that 92 out of 660 studies taken from 3 major surgical journals were actual cases of redundant publication. The rate of duplication in the rest of the biomedical literature has been estimated to be between 10% to 20% (Jefferson, 1998), though one review of the literature suggests the more conservative figure of approximately 10% (Steneck, 2000). However, the true rate may depend on the discipline and even the journal and more recent studies in individual biomedical journals do show rates ranging from as low as just over 1% in one journal to as high as 28% in another (see Kim, Bae, Hahm, & Cho, 2014) The current situation has become serious enough that biomedical journal editors consider redundancy and duplication one of the top areas of concern (Wager, Fiack, Graf, Robinson, & Rowlands, 2009) and it is the second highest cause for articles to be retracted from the literature between the years 2007 and 2011 (Fang, Steen, & Casadevall, 2012).

But is it misconduct in the context of non-academic science writing? I’m not sure, but I think it’s fair to say self-plagiarism counts as lazy writing. Whatever the ethics, let’s examine an essay by Pinker that Edge published sometime before January 10th, 2017, and match it up against Chapter 2 of Enlightenment Now. I’ve checked the footnotes and preface of the latter, and failed to find any reference to that Edge essay, while the former does not say it’s excerpted from a forthcoming book. You’d have no idea one copy existed if you’d only read the other, so any matching passages count as self-plagiarism.

How many passages match? I’ll use the Edge essay as a base, and highlight exact duplicates in red, sections only present in Enlightenment Now in green, paraphrases in yellow, and essay-only text in black.

The Second Law of Thermodynamics states that in an isolated system (one that is not taking in energy), entropy never decreases. (The First Law is that energy is conserved; the Third, that a temperature of absolute zero is unreachable.) Closed systems inexorably become less structured, less organized, less able to accomplish interesting and useful outcomes, until they slide into an equilibrium of gray, tepid, homogeneous monotony and stay there.

In its original formulation the Second Law referred to the process in which usable energy in the form of a difference in temperature between two bodies is inevitably dissipated as heat flows from the warmer to the cooler body. (As the musical team Flanders & Swann explained, “You can’t pass heat from the cooler to the hotter; Try it if you like but you far better notter.”) A cup of coffee, unless it is placed on a plugged-in hot plate, will cool down. When the coal feeding a steam engine is used up, the cooled-off steam on one side of the piston can no longer budge it because the warmed-up steam and air on the other side are pushing back just as hard.

Once it was appreciated that heat is not an invisible fluid but the energy in moving molecules, and that a difference in temperature between two bodies consists of a difference in the average speeds of those molecules, a more general, statistical version of the concept of entropy and the Second Law took shape. Now order could be characterized in terms of the set of all microscopically distinct states of a system (in the original example involving heat, the possible speeds and positions of all the molecules in the two bodies). Of all these states, the ones that we find useful from a bird’s-eye view (such as one body being hotter than the other, which translates into the average speed of the molecules in one body being higher than the average speed in the other) make up a tiny sliver of the possibilities, while the disorderly or useless states (the ones without a temperature difference, in which the average speeds in the two bodies are the same) make up the vast majority. It follows that any perturbation of the system, whether it is a random jiggling of its parts or a whack from the outside, will, by the laws of probability, nudge the system toward disorder or uselessness —not because nature strives for disorder, but because there are so many more ways of being disorderly than of being orderly. If you walk away from a sand castle, it won’t be there tomorrow, because as the wind, waves, seagulls, and small children push the grains of sand around, they’re more likely to arrange them into one of the vast number of configurations that don’t look like a castle than into the tiny few that do. [Enlightenment Now adds five sentences here.]

 

I could (and have!) carried on, demonstrating that almost all of that essay reappears in Pinker’s book. Maybe half of the reappearance is verbatim. I figure he copy-pasted the contents of his January 2017 essay into the manuscript for his 2018 book, and expanded it to fill an entire chapter. Whether I’m right or wrong, I think the similarities make a damning case for intellectual laziness. It also sets up a bad precedent: if Pinker can get this lazy with his non-academic writing, how lazy can he be with his academic work? I haven’t looked into that, and I’m curious if anyone else has.

To A Burnt-Out Activist

The scandal brewing at the end of my post has come to pass. This one hurt a little bit; publicly  at least, Silverman seemed to be in favor of policies that would reduce sexual assault, and spoke out against the bigots in our movement. In reality, given the evidence, he was talking the talk but not walking the walk.

That comes on top of my growing unease over that last blog post. There’s nothing in there worth changing, that I’m aware of; the problem is more with what it doesn’t say, and who it mentions in passing but otherwise leaves at the margin.

See, there’s a pervasive belief that minorities are responsible for bringing about social justice, either by claiming they created the problem or demanding they educate everyone. That falls apart if you spend a half-second dwelling on it. The majority, by definition, hold most of the power in society. If they accepted the injustice done to the minority, they’d use that power to help resolve it. In reality, they tend to bury their heads in the sand, ignoring the evidence of injustice or finding ways to excuse it, so their power is often wielded against the minority. The result is that the minority has to spend an enormous amount of time and energy educating and agitating the majority.

So you can see why calling for people to fight harder for the change they’d like to see, as I did last blog post, can seem clueless and even heartless. Yes, I placed a few lines in there to hint that I was talking to the majority, but those have to be weighed against the context I outlined above. This time around, I’d rather focus on the burnt-out activist than the clueless white guy.

Put bluntly, life is short. You should spend your time doing things you find rewarding; endlessly quoting painful testimony of sexual assault, or the science and statistics of how tragically common it is, or giving an embarrassingly basic lecture on consent, doesn’t stay in that category for long. The resulting feelings of burnout or frustration are entirely valid, and worthy of taking seriously.

Human beings are also complex, we exist in many cultures and movements. I sometimes advocate for secularism, but I’ve also written about science, statistics, and even dabbled in art from time to time. If one aspect of my life becomes frustrating, I can easily switch to another, and there’s nothing wrong with that switch. This may seem like a betrayal; how can you leave your sisters behind as they carry on fighting the good fight?

But it’s extremely rare for a single person to change a culture; in practice, change comes via a sustained, coordinated effort from multiple people. At worst, the loss of one person may slow things down, and even that is debatable: there’s an unstated premise here that once you’ve dropped out of culture, you can’t come back. That should be obviously false (and if it isn’t, run). If you can return, though, then why not use the time away to recharge? You’ll get a helluva lot more done ducking out from time to time to fight burn-out, than you would if you stuck around when you don’t care to.

I have tremendous sympathy for the people who are sick of arguing against all the sexism, racism, ableism, and so on within the atheist and skeptic movements. Take as long a break as you need to, come back if or when you feel it’s time. There should be an empty seat waiting for you, and if there isn’t you’ll be in a better place to flip everyone the bird and create a new culture that gets this shit right.


(As a side-note, I found it amusing when I began working through the OrbitCon talks and heard Greta Christina laying out similar points. She has been a big influence on my views on activism for several years, so the overlap is less surprising in hindsight.)

Sean Hannity?!?!

Context first. Trump has been fuming since investigators raided Michael Cohen’s offices and hotel room. It quickly became public that Cohen was under criminal investigation for “business dealings” possibly related to squashing reports of sexual improprieties. Nonetheless, Federal prosecutors have been secretly reading his email communication as part of said investigation.

There’s also been more reporting about Eliott Broidy, who used Cohen to pay $1.6 million to a woman he impregnated. Some interesting details started emerging: two of the women who had affairs with Trump, plus this third woman, all had Keith Davidson as their lawyer, who happens to know Cohen. The contract was with the same LLC Cohen set up to funnel money to Stormy Daniels, and it used similar wording right down to the code names. This adds to speculation that Cohen silenced so many stories of sexual assault that he had an organized system in place.

Both Michael Cohen and Trump have asked for “first dibs” in determining which documents are protected by attorney-client privilege, rather than the conventional “taint team.” More interestingly, again allegations by Federal prosecutors that Cohen had no real clients, Cohen’s provided a list of three: Trump, Elliot Broidy, and [REDACTED]. The latter explicitly asked Cohen and his lawyers to keep his name quiet. That came to a head today during a hearing in court today. Over on the Political Madness thread, SC and I have been tuning in via Twitter.

And, as you may have guessed by now, the judge ruled that Cohen’s lawyers couldn’t keep [REDACTED]‘s name sealed, so they were outed as Sean Hannity. Mayhem ensued; after all, Sean Hannity has been a big defender of Trump and condemned the raid on Cohen’s offices without disclosing his relationship. Hannity has had sexual harassment allegations leveled against him, and Fox News has promoted myths about sexual assault as well as a culture which tolerates sexual harassment.

There’s nothing public about Hannity that’s of the same scale as Trump or Broidy, however. Not yet, anyway; I expect a dozen investigative reporters are working to change that.

Go Local

Alas, Adam Lee beat me to this one, but it’s important enough to put on repeat.

For nearly two days, I had a strong flickering of hope. Buzzfeed’s article about Laurence Krauss came out, I was assessing the level of pushback, and I wasn’t seeing much of anything. Reddit threads were mixed, for instance. I went to the Friendly Atheist comment section expecting a cesspit, and was pleasantly surprised to see a mere stinkhole. Seeing a few diehard anti-SJW’s kick up a fuss is annoying, but it’s vastly preferable to a sea of neutral-ish randos. We’ve come a long way from the Grenade.

Then Sam Harris and Matt Dillahunty shit the bed, and CFI took over a week to suspend their relationship with Krauss (good) while stressing they follow their code of conduct (not buying that), and my hope that big organizations would make significant changes died.

But I was still left with a blog post, one that’s evergreen to these controversies. Each time a scandal pops up, I keep seeing people throwing up their hands and quitting the skeptic/atheist movement. While I have a lot of sympathy for the sentiment, and have even muted my own participation due to all the bullshit, I’d like to pitch the opposite idea: these controversies are precisely when you should be more involved.

Yeah, I know, you’d rather complain about the state of the movement, or claim there is no atheist movement because we’re too fractured. Problem is, by that metric there’s no feminist movement either: if you think atheism is fractured, look up the sex wars or the battle over radical feminism or the New Feminist movement or the debate between suffragists and suffragettes and so on and so on and so on. Gather more than one passionate idealist in a room, and they’ll quickly disagree on how to make those ideals come true no matter what the ideal or how many idealists you have. That’s the name of the game for any movement, progressive or otherwise.

You, as an atheist/skeptic, may not feel like you’re part of a movement because you’re not doing activism. That’s fine! But an atheist/skeptic movement still exists, whether you participate or not. Other people are still agitating on your behalf, and will be your representatives on the public stage. Because of that, these people are going to be considered the standard you are measured against. Hate to say it, but Laurence Krauss was right: Buzzfeed’s article is a smear on the atheist movement, because to most outsiders the movement consists of Krauss, Michael Shermer, Richard Dawkins, Sam Harris, Steven Pinker, David Smalley, Jerry Coyne, Sargon of Akkad, the Amazing Atheist, and so on. Shit on them, and by extension all atheists are shat upon.

This applies to organizations, too. The CFI board have been a rolling dumpster fire for years now, but why have they been? Big organizations have big bureaucracy, insulating the organizers from the grassroots and driving them to look for big investors who typically skew conservative. This makes them slow to respond and tough to influence from the outside.

Local groups are the opposite. It’s a lot easier to sway them in your direction, though admittedly this cuts both ways. Even if your local group is a Chernobyl, though, you can always route around them and start up your own. Best of all, CFI is gonna take a petition to clean up their act from Wichita Freethinkers a lot more seriously than one from Jane C. Rando; if you start small, you can work your way up and gain more leverage than you ever would if you walked away or stayed silent.

The same thinking applies to the “big names” of atheism. Avoid’m, call them out when they’re wrong, make it loud and clear that they’re not the only game in town. Instead, try to promote locals who have relevant expertise; their speaker fees will be a lot cheaper, if only because of the travel cost, there’s more variety, and that variety leads to more in-depth conversations. Conversely, think about becoming a speaker or activist yourself. Yes, it stinks that speaker lists tend to be dominated by the big orgs, but if you record yourself giving a lecture and shop it around to local groups, you might get a few takers. A lot of middle-level speakers already do this, to some extent, so why not join in the fun?

Not in the mood to join an org or exercise your vocal skills? You’ve still got a strong lever in your hands: money! Five bucks means a lot less to CFI than it does to the Black Freethinkers of Minnesota. Chipping in funds locally will go a lot further to holding events and bringing in speakers, and gives you a disproportionate voice in how things are run. Alternatively, find an activist who’s trying to make the community a better place and toss some cash their way. It’s the easiest, most effective option on this list, yet our bias to “doing something” blinds us to that.

The assholery in the skeptic/atheist community may be difficult to eliminate, but it’s easier than you think to marginalize.

Now, just to make sure this point isn’t lost: if you feel the need to step away, you have my full sympathies. No-one should be forced to be an activist, and self care is not selfish. If you do have the strength, though, consider channeling your anger and frustration into pushing back.

Need more specific guidance? Tell you what, I’ll de-evergreen this post and name some specific people and organizations that I think are worth listening to, inviting, helping, or paying. Besides, it’ll be something to point and laugh at when Monette’s outed as three kids in a trench-coat.

Organizations

Freethought Blogs, The Orbit, and Skepticon: Oh hey, did you know that those three organizations and a few individuals are being sued by Richard Carrier? He’s grumpy they talked about his bad behaviour, apparently, and the legal fees are leeching away funds that could be used for other things. If you like what they do, toss some cash into their respective fundraisers. I know you’re sold on FtB if you’re reading this, but The Orbit also has cool bloggers too like Stephanie Zvan and Miri and Tony Thompson and Ania Onion Bula. I’ve been to Skepticon, and can vouch for their excellence. Oh, and they’re free to attend despite their massive size!

Secular Woman: This group loves to be a thorn in the side of the big orgs, most recently getting kicked out of the club for complaining too much. I’ve been an on-and-off member for years, attended their last conference, and been quite happy with their work. Attend their next conference or become a member, you know you want to.

Speakers

Honestly, you could do a lot worse than writing down the names of everyone participating in OrbitCon: Valerie Aurora, Jennifer Beahan, Brianne Bilyeu, William Brinkman, Chrisiosity, Greta Christina, Heina Dadabhoy, Eiynah, Debbie Goddard, Alyssa Gonzalez, Olivia James, Alix Jules, Lauren Lane, Trav Mamone, Marissa Alexa McCool, Monette Richards, Ari Stillman, Steve Shives, Mandisa Thomas, Kristi Winters, Callie Wright, Jessica Xiao. If these people are willing to set aside some time to chat online to a general audience, there’s a good chance they’d be willing to Skype into your group for a lecture.

I’d also like to add Sikivu Hutchinson, Lilandra Ra, Annie Laurie Gaylor, Alex Gabriel, James Croft, Marcus Ranum, and Crip Dyke. I could keep going, but I should really get this post out the door before the next controversy arrives. In the meantime, you know what to do.