Books to Read While the Algae Grow in Your Fur, March 2022

Attention conservation notice: I have no taste, and no credentials to opine on the sociology of education, political and moral philosophy, medieval Islamic science, or even, strictly speaking, pure mathematics.

Dana Stabenow, A Cold Day for Murder, A Fatal Thaw, Dead in the Water, A Cold-Blooded Business, Play with Fire

Mind candy mysteries, where the Alaskan environment is as much a character as any human being, or husky. Stabenow was, I believe, originally a science fiction and fantasy writer, and I think some of that comes through in the way the very strange world of Alaska is unfolded before the reader. It also comes through in the character of Kate Shugak, a hero of basically-royal birth who lives on the border between civilization and the wilderness, and who roams the countryside defeating monsters and malefactors, especially those who have offended against the laws of kinship and hospitality. (There are a lot of explicit references to Greek myths and I do not believe any of this is coincidence or even unconscious.) The fact that I read five of these in a month, and have more in the queue, tells you how easily they go down. §

Douglas B. Downey, How Schools Really Matter: Why Our Assumption about Schools and Inequality Is Mostly Wrong

I am not sure what to make about this one.

Downey studies some nationally-representative longitudinal data sets, which measure student achievement in reading and math at multiple points in the school year, over multiple years. "Longitudinal" here means that each student is being measured multiple times, allowing one to draw inference about how much was learned when. The basic finding Downey extracts from this is that during the school year, richer and poorer students, and black and white students, learn at basically the same rate. But they arrive at school at very different average levels of achievement, and their gaps grow while out of school each year. Thus, on this evidence, schools for the disadvantaged are in fact doing about as well at teaching reading and math as other schools. The inequality in educational outcomes, then, isn't due to inequality in schooling, but to (as Downey puts it) the other 87% of students' lives.

This is remarkably contrary to received opinion, what Downey calls "The Assumption", that schools for the poor are poor schools which do not teach effectively. I get the impression that Downey started by wanting to be talked out of this position, but came to embrace it for lack of intelligent opposition:

I don’t think that the people questioning the evidence are bad people, but they are reluctant to let go of the dominant narrative about schools. It would be one thing if the reason was because they had issues with whether the ECLS-K item-response theory scales of reading can be considered truly interval, or if they questioned whether nonschool investments in children are constant across seasons, or if they thought that the approach scholars use to model the overlap days between test dates and the beginnings and ends of school years was insufficient. ... But while many have resisted the empirical patterns in chapters 1--4 and remain committed to The Assumption, the quality of evidence doesn’t seem to be the obstacle. [p. 97]

I join Downey's audiences in astonishment. I also join him in thinking that "we really need to reform the distribution of rewards in the broader society", but I just have a hard time swallowing it. (Among other things, if he's right, why are parents so convinced otherwise?) But I also don't have any clever explanations to make this pattern in the data go away. As a statistician, I do wonder about whether these surveys really cover a nationally representative sample of students and schools. There is also the issue (which Downey highlights in the quote above) of whether these reading and math scores are really "interval". Concepts like "median" make sense with merely ordinal variables, but something like "the change in the median poor kid's reading score from September to May is equal to the change in median scores for rich kids", \( X_p(2) - X_p(1) = X_r(2) - X_r(1) \), needs us to be able to compare differences at arbitrary points along the scale. So this is resting a lot on the ways the survey researchers translate students' answers into numerical values, and I'd ha