Only Thugs Need Apply

My social media feed has been replete with this meme:

Probably not.

One of the ironies of the (I hope) now-winding-down BLM era is that the two egregiously racist mass murders of blacks didn't provoke much in the way of a lawless reaction. Unless I completely missed it (and correct me in the comments if I did), there wasn't any rioting in the wake of either the Charleston church shooting or the Buffalo supermarket shooting. Even in the specific category of deaths at the hands of law enforcement, again the most egregious case of John Crawford III got a small, quiet demonstration from, IIRC, white middle-aged members of Buckeye Firearms, and exactly nothing from his fellow blacks.

Rather, blacks reserve their propensity for riot and mayhem for career criminals who get their comeuppance. Michael Brown and Jacob Blake violently resisted arrest and were deservedly shot. Eric Garner and George Floyd passively resisted arrest and died by accident. Granted Freddy Gray should have been better secured in the back of his paddy wagon, but he certainly deserved to be there. All these guys got riots.

My theory for this -- only a hypothesis, really, since I don't have any direct evidence -- is that these riots are stoked by Antifa operatives under circumstances such as will be maximally polarizing. But my point is that a white lowlife murdering a pretty black girl on the train, were it to ever actually happen, would probably not set off a riot.

Social vs. Physical Danger

Some of the abundant commentary on the murder of Iryna Zarutska has focused on her apparent lack of situational awareness. Steve Crowder, for instance, discussed it at length yesterday. This is surely correct, but it occurred to me that while Iryna's behavior was poorly suited to protecting her from physical hazards, it did seem optimized for protecting her from social hazards.

Watching the video of her last moments, I was reminded of the scene from the movie Anora (free on Kanopy) where the title character is riding the subway home from her job at the strip club. (Apologies in advance if my vocabulary for this sort of thing hasn't been updated since the '80s.) The film leans hard into the contrast: on the clock, Anora the prostitute is warm and charming as customers stuff money in her g-string; off it, she wears baggy clothes, clamps her ears with over-the-ear headphones, clamps her face with a thousand yard stare. All calculated to convey the message: do not even thing about talking to me.

Likewise, Iryna. Whatever she might have been looking for in her personal life, she quite reasonably believed she wasn't finding it late at night on the subway. So she tucked her face under a ballcap (a style choice that I noticed had become common among young women at the gym a few years ago) and absorbed herself in her phone: don't even think about talking to me.

Religiosity vs. Time

In reference to a Pew study on intergenerational religiosity, Scott writes:

Contra compelling anecdotes, only ~5% of people raised very religious end up atheist later in life (X). Most people are about as religious as their parents; most exceptions are only slightly less religious, and most families that secularize do it over several generations.

Okay, but those generations add up.

Here is the Pew data as a matrix. I have reversed the row order such that both row and column indicies increase with religiosity. I have also subtracted 1% from element (4,4) such that the table sums to 1.0.

PEW =

0.0600	0.0100	0.0100	0
0.0400	0.0800	0.0300	0.0100
0.0300	0.0900	0.1600	0.0300
0.0200	0.0500	0.1400	0.2400

Note these are joint probabilities. To find the religious probabilities of the parent generation, we must sum all columns in each row (all code is MATLAB):

P_parent = sum(PEW,2);

P_parent'

ans =

0.0800 0.1600 0.3100 0.4500

To find the religiosity of each subsequent generation, we need the conditional probabilities, which we can derive from Bayes' Theorem:

P_child_given_parent = PEW./repmat(P_parent,1,4) 

P_child_given_parent =

0.7500	0.1250	0.1250	0
0.2500	0.5000	0.1875	0.0625
0.0968	0.2903	0.5161	0.0968
0.0444	0.1111	0.3111	0.5333

Finally, we construct a table showing the generational change over time:

for gen = 1:10

    R_gen(gen,:) = P_parent' * P_child_given_parent^(gen-1);

end

R_gen =

0.0800	0.1600	0.3100	0.4500
0.1500	0.2300	0.3400	0.2800
0.2153	0.2636	0.3245	0.1966
0.2675	0.2748	0.3050	0.1527
0.3056	0.2763	0.2899	0.1281
0.3321	0.2748	0.2795	0.1137
0.3498	0.2727	0.2726	0.1048
0.3616	0.2709	0.2682	0.0993
0.3693	0.2695	0.2653	0.0959
0.3743	0.2686	0.2635	0.0937

Which we can plot:

So, clearly, the Very Religious are the long-term demographic losers. My first thought was that we could make this up in volume, but then I realized that the initial table confounds our greater on-average fertility with the starting percentages; this is a poll of the children, so the children of very religious families were already more numerous among the respondents, assuming the poll was representative. My calculation of the conditional probability assumes fertility is equal, but I'm pretty sure I would need additional fertility data and a way to map it on to these categories in order to correct this.

My second thought is that these statistics might be biased with respect to "family" religiosity: it is plausible that children's characterization of their family's religiosity may be colored by their own. For instance, a child from a "somewhat" religious family who is personally "not at all" religious, might exaggerate family religiosity to "very". OTOH, I'm not sure this matters for predicting the trend.