I’ve decided this year that to try writing brief weekly summaries about what I’m doing, reading, working on, etc. The purpose of these posts is just to get myself into a bit of a rhythm of writing and spending more thoughtful time doing things that are meaningful to me. I’ll try to publish these on Saturdays, but that will be modulated by my work/travel/energy levels.

What I’ve read

• Racial Discrimination in the United States: A National Health Crisis That Demands a National Health Solution

This academic paper calls for an investigation on the epidemiological effects of racism, citing evidence that health outcomes are worse in racial minority communities, largely due to the effects of racial discrimination.

Systematic reviews and meta-analyses provide support for the notion that racial discrimination is related to multiple forms of illness, including depressive symptoms, anxiety, post-traumatic stress disorder, hypertension, and diabetes. These negative health consequences may be exacerbated during adolescence, a developmental transition marked by enhanced socioemotional processing. Racial discrimination is especially harmful in countries such as the United States that have a pronounced history of racism.

Racial violence and systematic white supremacy unsurprisingly manifest in public health. Too often we see that low-income housing in predominantly Black communities exhibit negative living conditions, such as poor or non-functioning heat, black mold, and of course, Flint still doesn’t have clean water. This of course should be expected to have health impacts, and the article is sure to note that the effect is more pronounced in adolescents.

• Highway to the Danger Zone: The Heterosexual Tragedy of Top Gun

This hilarious satire frames Top Gun in the kind of language that straight critics use to describe queer film. Presuming that the film was a homosexual love story set against the backdrop of Naval Aviation in a world where heterosexuality was seen as deviance, the piece hits all the right notes: the secrecy between Maverick and Charlie’s relationship; the fatal queerstraight in Goose; and the role of the wingman in dating and other life successes.

He continues to experiment with heterosexuality, until finally, he’s forced to reevaluate his life and choices. Goose meets a tragic end during a scene where some very cool Airplane Moves go very wrong, and in that moment, the only definitely-heterosexual character in the film is gone. Although Goose’s death is accidental and unrelated to his lifestyle, it cannot be denied that his demise represents a heterosexual tragedy. Without his influence, Maverick struggles to find direction—in his career and in his relationship with Charlie.

Perfect.

• Beyond Cherry-Picking

There’s something broken about statistics, and about how we use data, and about how we design experiments. We focus too much on effects vs models, and we aspire towards bigness even when bigness isn’t where we need to be focusing. By using a large data set and formulating hundreds of thousands of hypotheses, this research team uses the statistical inevitability that statistically significant effects would be found to highligth the absurdity with which we are performing resarch these days.

More importantly, however, they also call out how these types of findings make headlines and affect policy decisions, decisions which affect millions or billions of dollars in research funding each year.

Although statistical significance is often used as an indicator that findings are practically significant, the paper moves beyond this surrogate to put its findings in a real-world context. In one dataset, for example, the negative effect of wearing glasses on adolescent well-being is significantly higher than that of social media use. Yet policymakers are currently not contemplating pumping billions into interventions that aim to decrease the use of glasses.

This has, in my opinion, relevant links to data science. The big data revolution still hasn’t quite panned out like many said it would, and a big part of that may be a consequence of many organizations not truly having big data, or when they do, having big enough data that it is impossible to escape ridiculous conclusions. In my opinion, we’ll eventually see a shift back towards model-driven techniques. This is not to say that big data is dead, but it is to say that it’s more than big data that we need to think about.

What I’m reading

• The Magnificent Ambersons, Booth Tarkington (part of my Modern Library project) Progress: 3%
• Short Stories in German, Olly Richards and Alex Rawlings Progress: 38%
• Linear Algebra Done Right, Sheldon Axler (see my working progress here)Progress: 10%

The week ahead

Thankfully, I’m not traveling this week! I have some time to focus on work, and my goal is to clear some First Vigil backlog. I think I can finish clearing through the research pipeline this week. Also, I have some help with that now, and it’s been great to see the project move forward with other contributors. I want to finish through Chapter 3 of Axler, and get some more pleasure reading done. Mostly this week I’m focused on getting back to progression content in my own life.