Radical discrimination

At the end of the "Applications of Logotherapy" chapter of The Will to Meaning, Viktor Fraankl writes as follows:

   "Thousands of years ago mankind developed monotheism. Today another step is due. I would call it monathropism. Not the belief in the one God but rather the awareness of the one mankind, the awareness of the unity of humanity; a unity in whose light the different colors of ours skins would fade away. "

 Frankl's foot note on this read: "I am in no way against discrimination. To be sure, I am not for racial but rather for radical discrimination. That is to say, I am for judging each individual on the grounds of the unique 'race' that is represented by him alone. In other words, I am for personal rather than racial discrimination."

Related post: https://uncommoncontent.blogspot.com/2020/07/making-life-meaningful-even-when-facing.html

https://uncommoncontent.blogspot.com/2013/11/its-meaningful-life.html

The Bambi Backstory

While reading Nathalia's Holt's The Queens of Animation: The Untold Story of the Women Who Transformed the World of Disney and Made Cinematic History I learned a number of surprising things about the development of Disney's films - both the most iconic and the ones that do not survive on video because of their overt racism (ike Songs of the South) or the ones that have been censored for the same reason (like Fantasia).

But for me the most surprising  revelation of the book was about a Disney movie I've never even seen. Before Bambi made it to film, it was a novel written by Felix Salten and called Bambi: A Life in the Woods.

The author was born in Budapest in 1869, though his family moved when he was a month old to Vienna. The motive, as Holt explains (96) was to gain greater opportunity in a city that had begun granting some rights to Jewish resident in 1867.

Salten had a strong sense of Jewish identity that found expression in his passion for Zionism. Like the fictional Mordechai in George Eliot's Daniel Deronda, he was a big advocate for a Jewish homeland.

Salten's novel first saw print in the form of newspaper serialization in Vienna in 1923.

Just over a decade later, the copies of the book were thrown on Nazi bonfires.  Holt  explains (p. 26), "The book was banned not only because of the author's Jewish heritage but also for its metaphors about anti-Semitism. "

While this is a revelation for people familiar with the deer as depicted by Disney animation, back in the early 20th Century, people understood the allegory. Even the description of the butterflies touches on the experience of people who cannot find any permanent home "'always searching further and further because all the good places have already been taken,'" Holt quotes (26).

 Bambi was a means of exploring the themes related to Jewish identity and surivial.  The deer in this story are not carefree creatures but burdened by the persecution they endure from hunters and who have to consider if assimilation is the path to take to survive.

 While some animals adopt "If you can't lick 'em, join 'em" to try to survive with hunters, that strategy fails them, as illustrated by a character who believes wearing a halter will protect him who ends up shot by a hunter. Likewise, the dogs in the novel are described by Bambi's father thus: "'They pass their lives in fear, they hate [Man] and themselves and yet die for His sake'" (quoted in Holt 96).
Though Holt comments on how very appropriate that was for the time at which Bambi is made, it really is a perennial theme for people who have repeatedly been displaced and threatened.

The book is interesting also for the glimpses into working as a woman in what was very much a man's field, and the bad old days of sexism, racism, and persecution of Asians under Roosevelt. If aviation is also of interest, you may enjoy her tangents about the animators who also aspired to fly and their achievements in the air as well as on film.

Sex, Lies, and Data Profiles

The title of this blog could have been the title for Seth Stephens-Davidowitz's book Everybody Lies. As he explained in his interview with Freankonomics' Stephen Dubner, he knew the title is inaccurate, though he was told that "98% of people lie" wouldn't sell well. In case you were wonderings about the Cretan or Liar's Paradox implied by the title he opted for, he assures the interviewer that he is among the 2% of honest people. But didn't he then lie in the title? And is he really as honest as he claims? This blog examines the second question.

Ultimately, what makes both data reports from people who are presented as experts and data visualization so effective at conveying a point is that they don’t require much analysis on the viewer’s end because they’ve already done the thinking for you. That’s both seductive and potentially misleading.

That’s exactly why we have to be careful about not merely accepting the visually expressed story at face value. Any data visualization should be subjected to a triple C test with a check for context, correlation, and causation that I wrote about for Baseline  here.

In light of how openly political media and companies that handle data have grown, there's clearly a need for a few more C words to keep in mind when presented what is offered as objective data:

• Correspondence to reality. Just because someone claims expertise doesn't mean they are completely correct about their assertions. For example, when I was in labor with my first baby, the doctors and nurses at the hospital just dismissed my pains, claiming the contractions were "mild" and that the birth was far from imminent. I was not the expert; they were, but I knew that I felt the baby coming. As it turned out, the resident barely got to me in time. I learned from that experience that you should not be gaslighted by expert views that directly contradict not what you just think you know but what you do know and directly experience. 
• Convenience: This pertains to both means and ends. Convenience of means refers to using the data that is on hand or easily measured even if it's not necessarily the data that is the most relevant. It's rather like measuring how much snow fell on your windowsill because it's easy to reach rather than going out to get the measure on the street and in drifts to get a more accurate measurement. Convenience for ends is about selecting data that you can easily fit into the conclusion you wish to draw AKA cherry picking. 
• Confirmation Bias:In general, when you look for data on something, you have to bear in mind that absolute objectivity is rare. Many of us have deeply-seated values and beliefs that will not allow us to entertain the possibility that we are on the wrong track,which would skew our results because of what we allow and disallow in the data set. It is the equivalent to painting a bull's eye around where your arrow went. So ask yourself, does the person have some personal agenda that could be coloring the outcome? If so you should treat them with the same healthy skepticism you would treat cigarette tobacco studies sponsored by tobacco companies. 
• Certainty Camouflaging Contingencies: Few things are absolutes, so if someone states something without qualifiers, likely something is being hidden or glossed over -- like the fact that the data is out of date or taking searches of racist terms and jokes as proxies for the person being a racist and then shifting labels from what actually is measured to what the person says is signified by the measurement. This leads to a triple F: Fudging Figures and Facts.

Incidentally, Seth Stephens-Davidowitz takes no chances that you won't recognize him as an expert. Right on p. 1, he declares, "I am an internet data expert." I don't make any such claim, though I have been delving into question of big data since 2011 and regularly review data science student work. But unlike Stephens-Davidowitz, I didn't work for Google. It was actually a team from Google that originally inspired me to write up the piece on not believing everything you see in data visualizations.

The data visualization a the beginning of Everyone Lies( p.13) presents two maps of the US that intends to show a correlation that implies causation. Stephens-Davidowitz refers to having researched correlations of racist searches with voter patterns to argue that Obama lost votes to racism. However the argument he makes about Trump right at the beginning of his book is actually based on an assertion that Nate Silver made in a tweet in early 2016.

Nate Silver's tweet cites an article written by a different Nate with the last name Cohen to bolster his claim. So I went to his source: a New York Times article written by  published on December 31, 2015, Donald Trump’s Strongest Supporters: A Certain Kind of Democrat, and there are the maps  that appears in Stephens-Davidowitz's book.

The maps that are juxtaposed to indicate correlation and imply causation in the article that reappear (in grayscale) in Everybody Lies. In case the caption appears too small for you to read, I'll put it in text: "Source: Vote estimates by Congressional district provided by Civis Analytics; Google search estimates from 2004-7 by Seth Stephens-Davidowitz. How convenient! Stephens-Davidowitz already had that data set from when he gathered it to present evidence of racism at the time of Obama's election. So what if it was really past its sell by date in 2015, never mind in 2017, recycling is a good thing, isn't it?

Aside from the lack of color, there are two other differences in the maps that appear in the book: One it doesn't have the identifier by year. Two: the more cautious label applied in the newspaper illustration of "Where racially charged Internet searches are most common" is replaced by the more confidently asserted "Racist Search Rate." If you suspect that they are, in fact, different maps, I can only tell you to open the book and look for yourself to be assured that I am not misrepresenting anything. This is an example certainty camouflaging contingencies.

Here's my simple Venn diagram of an assertion Stephens-Davidowitz made in asserting in the live presentation, as he did in his book, that the biggest single predictor of a vote for Trump was being a racist. The two circles overlap almost completely.

My own illustration of Seth Stephens-Davidowitz's contention

But if you start looking at the data he used to justify this conclusion, you see it's not at all this simple.


It's true that Nate Cohen is hoping to insinuate the Trump support includes areas that tend to more racist, though he is smart enough to qualify the argument: "That Mr. Trump’s support is strong in similar areas does not prove that most or even many of his supporters are motivated by racial animus. But it is consistent with the possibility that at least some are. "


The article also reflects understanding that things are really not so black and white in Democrat vs. Republican presidential elections: "Many Democrats may now even identify as Republicans, or as independents who lean Republican, when asked by pollsters."


Remember the NY Times' article title? That's the main argument, not really the twist that Silver gave it, as many replies to his tweet pointed out: "Mr. Trump appears to hold his greatest strength among people like these — registered Democrats who identify as Republican leaners — with 43 percent of their support, according to the Civis data"

While the article merely suggests that racial attitude could be involved, Stephens-Davidowitz goes even further than Nate Silver's tweet, asserting that racism is the strongest indicator of a Trump vote. That is what he said in the live presentation I heard on April 19th. At the end of the event, I went up to him and asked how is it possible to link the person who searched for things like racist jokes with votes for Trump.

He admitted that would be impossible. Instead, he said, they look at the areas where Trump won and correlate that with areas where there have been searches he identifies as racist to draw this conclusion that racism was the definitive motivating factor in votes for Trump.

 He indicated that the correlations were made based on the verified fact of which states voted for Trump correlated with the type of Google searches that, he contends, identifies a person as racist, and that was conclusive enough for him.

What he failed to admit was that the correlations were not made on the basis of actual voting results but on earlier maps of projected Trump support.

 A look at the actual map of the election results shows a different story. The predictions included just a fraction of the states that did go to Trump, which means they failed to represent the voters overall, a serious failing in what is presented as comprehensive and accurate data.


Let's take a closer look. The maps paired by Stephens-Davidowitz imply a correlation between his findings of data searches that ended in 2007 with the Trump support assumed to be in place in 2015.So remember all the steps of remove we have here:

1. 1.We have search data for racial jokes, the n-word, and the like, on which basis we are to assume that all (or at least most) voters of a particular state can be characterized as racist overall if the percentages of such searches are higher than average.
2. 2. Furthermore, we must assume that in the course of nearly a decade, all the racists stayed in place and retained their views.While such an assumption of stasis may have worked a hundred years ago, it is somewhat doubtful that it can hold in the 21st century when things move at broadband speed.
3. 3. We have to consider that the overlap of higher support for Trump and higher racism rankings are not just a correlation but an indication of a causal relationship, as he explicitly identified it as an accurate predictor.

The whole theory could possibly be woven together to appeal to those who already favor that outcome, but it doesn't hold water. Even if I'd grant Stephens-Davidowitz that most of the people who had conducted those internet search about a decade before the election stayed in place and did cast their vote for Trump, the visualization would look like this:














Of course, that is not wholly accurate either, as we don't have a clearly established relationship between the people who made racially charged searches back when their search data was collected and the voting citizens of the area in 2016. But this represents the fact that even if some racists are including in the voting pool for Trump, it doesn't define all the voters in that pool. As we can see from the maps of actual voting results with Stephens-Davidowitz's own map of racism, Trump voters were not confined to those states. See the comparison shown by the juxtaposition below.

This reveals that the correlation that Stephens-Davidowitz's points to is not nearly as causative as implied. First of all, some of the places he identified as leaning toward racism in the west actually voted for Clinton. Second of all, the states that did vote for Trump far exceed the ones identified as inclined toward racism. So inclusion of some states with racist searches in the Trump wins is not a definitive correlation because it only includes a portion (not a definitive majority) and fails to account for the voters overall.


Ultimately, what Stephens-Davidowitz's set of maps really shows is not conclusive proof that racism is the best predictor of votes. Instead, what we have in the argument is an illustration of of a confirmation bias that clings to outdated, misleading, and factually wrong representation even when we have access to data that disproves the theory. 

While maps of actual votes were available by the time he published his book,  he kept the map of incorrect predictions as the definitive map of Trump votes because it fits the hypothesis better. It just didn't fit the reality because with that limited support, he would not have won the election.

Sticking with old data sets because they are convenient -- both in terms of saving you research time and in terms of fitting what you want to prove -- is not true data science as it runs contrary to the essential value of science. Richard Feynman touched on this issue in a 1974 address to Caltech entitled Cargo Cult Science in which he explained that true science is about doing one's best "to give all of the information to help others to judge the value of your contribution; not just the information that leads to judgment in one particular direction or another."

This is not to say that we must put Trump on a pedestal.No matter whether you love the president,hate him, or like many others, are somewhat neutral and willing to judge based on results, you should still not distort data to support a particular narrative.

At this point, you may be thinking, "Well that's all politics, but what about the sex in the title?" It's there because Stephens-Davidowitz uses examples related to that to try to capture attention, as demonstrated by what he starts with in his book, his interview (cited above) and the live presentation I heard.

I didn't take on the deconstruction of that, though someone else did. See the second part of Chelsea Troy's review, Everybody Lies’ Review Part 2: Dangerous Methodology She brings up the issue of bad proxies and misleading numbers. I couldn't agree more with what she says here: "Just because there are some numbers floating around doesn’t make a study valid."

To Boldly Go Beyond Barriers

In the 1964 picture below, the computer is on the right, and her name is Melba Roy She went on to become Program Production Section Chief at Goddard Space Flight Center. The machine next to her was referred to as an IBM then.

pic from 

commons.wikimedia.org/wiki/File:Melba_Roy_-_Female_Computer_-_GPN-2000-001647.jpg

Melba Roy does not appear in the film Hidden Figures, which concentrates its attention on just three of the African-American women who worked as computers in the space program, though she is mentioned in Margot Lee Shetterly's book on which the movie is based. The book covers a much longer span of time and more characters than the main three: Katherine Johnson, Dorothy Vaughan, and Mary Jackson.

One female of color who doesn't make it into either the film or the book is Janez Lawson. In fact, it's hard to find anything about her at all beyond what has already been unearthed by Nathalia Holt in her book The Rise of the Rocket Girls.  In truth, I found Shetterly's book a faster read, but there is more information in Holt's about women in the industry and how the role of computer became a sort of pink collar career.

In fact, though, you need to go even further back in time to see women employed as computers. And they were also focused on the stars. That's the topic of  Dava Sobel's book The Glass Universe: How the Ladies of the Harvard Observatory Took the Measure of the Sky.Working painstakingly through photographs of telescopic view of the heavens, these computers observed differences in spectra and worked out the classification system that shifts the alphabet around. The mnemonic device became "Oh be a fine girl, kiss me." New discoveries have contributed to new letter placement, leaving some question as to how to complete that famous mnemonic. I have to admit this book can be slow going, though it does have some nice photographs to illustrate the history, something that is also in Holt's book but missing in Shetterly's.

Another thing in those two books that Shetterly doesn't detail are some details about what the women were paid. One of the key women in the Observatory notes that she was only paid $1500 when men in comparable positions were paid $2500. Other women employed as computers were paid hourly, at the rate of 25 - 30 cents. This hourly rate must have remained the standard, as Holt says that's how the women working at NASA got paid. As a result, some of the women earned more than their husband because of the long hours they had to work.

 In contrast, Shetterly's book always states their earnings in yearly amounts, and the film indicates that Katherine would not have been paid any more for staying later at work. It is possible that her pay grade was changed even though she was still called a "computer."

As the Hidden Figures  book picks up the history of the computers in the 40s, it includes Miriam Mann, a contemporary of Vaughan who is described as a woman as petite and fearless. Mann repeatedly ripped down the paper sign designating a section of the cafeteria for the "colored" women. It would go back up, and she'd rip it down again until it stayed down.

Katherine Johnson with celestial Training Device
Pic from https://www.nasa.gov/image-feature/katherine-johnson-at-nasa-langley-research-center

 If you've seen the film, you'd realize how her defiance was translated into a somewhat different context with a great deal of dramatic license. For instance, Katherine Johnson achieved major recognition as the first female computer to get her name on a report as early as 1959 (published in 1960) that predates the movie. But as the center of the film, her character is subjected to Jim Crow practices in ways that didn't happen in real life. In the book, she is the "unflappable" Katherine who was never driven to an outburst about the bathrooms because she acted on the assumption that she had the  right to use the restroom in the building in which she worked. There's no indication that she was prevented from doing so in the book. I

In point of fact, it was Jackson who experienced that kind of humiliation. But she only put up with it once (Shetterly 108). Once was enough to get her to rant about her situation (not in a room full of people) in front of one man who offered that she work for him. She accepted. 

Another cool thing about Jackson's achievement as an engineer was not  breaking the color barrier at the school for her classes, but serving as an inspiration for her children. It's a pity the movie didn't include this episode in the book. When Jackson's son won the Virginia Peninsula Soap Box Derby, he declared, "'I want to be an engineer like my mother'" (200).

Dorothy Vaughan gets a lot more coverage in the book than in the film, as she began her computer career about two decades before the the movie opens. She actually did earn official recognition as head of the West Ara Computers unit" but lost it at the end of the decade when the unit was disbanded.  It's ironic to note that near the end of the book (264) Shetterly notes that Vaughan never learned to drive, making the carpooling scenes thrown in to show the closeness of the characters to have been historically impossible.  The library book event is not in the book either. FORTRAN was actually taught to the employees, and the on-site classes were open to all races (139). But again, that's dramatic license for you.

Dramatic license isn't necessarily bad, one just has to realize that events were a bit different in fact. On the plus side, the film is very engaging and actually presents the story in a way that works well as an introduction of the subject that can work even for young children. They would not be subjected to hearing racial epithets and they won't even see a single character smoke, though the sixties was a time when most people held a cigarette at some point during the work day.

BTW the title's Star Trek reference is deliberately placed because Shetterly refers to the popularity of the show with the NASA set as well as with Dr. Martin Luther King, Jr. She refers to his encouraging Nichelle Nichols to stick with her role as Lt. Uhura  when she wanted to quite the show because he saw her as such a positive role model. That's something the actress recounted in her autobiography and even gets quoted the Wikipedia entry about her. Fictional characters can be inspiring, but sometimes real people prove equally impressive.

Additional online resource: http://omeka.macalester.edu/humancomputerproject/ 

 Interesting link about other women's roles in space: http://www.neatorama.com/2013/04/15/Women-in-Space-The-Mercury-13/

On the Hidden Figures Exhibit https://www.nasa.gov/feature/langley/museum-exhibit-highlights-nasa-langleys-human-computers-from-hidden-figures