School book knowledge…

School books can be gems… here’s a timeline of important events, apparently. Quite a particular perspective on the world, and a particular perspective on science and inventions, where the typical case is one person (usually a man) single-handedly achieved something.

Young World 4, Activity Book, © 2015 Klett und Balmer Verlag

Like “James Watt invented the steam engine.” James Watt shares the honour with Matthew Boulton on the £50 note. For inventing the steam engine? Nope, for making “revolutionary changes to the efficiency of the steam engine”, and Boulton to “market steam engines”.

Wikipedia knows of historical precedents from the first century AD, and Thomas Savery as the first to use a steam engine commercially.

Wikipedia

I recognize that school books need to simplify and leave out details, but we can also simplify in a way that doesn’t pretend that history is the act of “great men”. A language book is maybe not the place to nitpick about stuff like whether a railway line in 1845 that ended in Switzerland should be counted, rather than the first internal line in 1847 which is mentioned, or discuss that there are other ways to count the length of World War II.

But why is there Ferdinand Magellan all on his own, who didn’t actually complete the circumnavigation, and none of the 200+ staff (some of whom actually did sail around the world)?

Hopefully, I need not say much about “1492 Christopher Columbus discovered America.” But it struck me how Columbus is greatly under-credited in history: Not only did he “discover” America (never mind indigenous peoples, never mind Norse colonization), but he can obviously see into the future (I’ve never seen him credited for that…!): when he discovered those lands, he already knew that 15 years later two German cartographers would name those lands after Amerigo Vespucci and the name will stick. ⸮⸮⸮

OK, we’ll probably want to leave irony out of school books in primary school, but can we try harder? I’m all for simplification, but perhaps a less Eurocentric one where we don’t celebrate individuals and ignore everyone else who contributed…?!

We have no idea — same analysis, different results

In a recent paper, Akira Igarashi and James Laurence look at anti-immigrant attitudes in the UK and Japan. Like my 2019 paper in JEMS, they and how they highlight the limited research on non-Western countries, but they analysis they do is much more similar to what Sjoerdje van Heerden and I did in Urban Studies. Them like us relied on panel data to get a better handle on changing attitudes to immigrants. Them like us looked at the share of foreigners in the area (this relates to theoretical expectations that individual attitudes to immigrants reflect changes in the share of foreigners in the area; we refer to the same theories). We both used fixed-effect panel models. They find that “increasing immigration harms attitudes towards immigrant”, while we report that “a larger change in the proportion of immigrant residents is associated with more positive views on immigrants among natives” — yes, the exact opposite!

Need another example? Several studies examine the impact of sudden exposure to refugees on attitudes to immigrants and votes for radical-right parties. Such sudden exposure happened for example in Austria and Germany in 2015. In separate analyses, Andreas Steinmayr 2020 finds a clear increase in support for the radical-right, as we find in the work by Lukas Rudolph and Markus Wagner. Max Schaub, Johanna Gereke and Delia Baldassarri, by contrast “record null effects for all outcomes”. Same situation, same strategy to obtain the results.

We could now start the detective work, examining the small differences in modelling, ponder about the impact of how we define neighbourhoods, invoke possible differences between the countries (are the Netherlands an expectation, when the UK and Japan yield the same results? — not likely). Or we could admit how little we know, how much uncertainty there is in what we do, how vague our theories are in the social sciences that we can come to quite different conclusions in quite similar papers. I guess what we can see here is simply a scientific search for answers (it’s not like our research output would otherwise disagree so clearly). It’s probably also a call for more meta-level research: systematic analyses that synthesize what we do and don’t know, because even though individual papers sometimes contradict, we know quite a lot!

Heerden, Sjoerdje van, and Didier Ruedin. 2019. ‘How Attitudes towards Immigrants Are Shaped by Residential Context: The Role of Neighbourhood Dynamics, Immigrant Visibility, and Areal Attachment’. Urban Studies 56 (2): 317–34. https://doi.org/10.1177/0042098017732692.

Igarashi, Akira, and James Laurence. 2021. ‘How Does Immigration Affect Anti-Immigrant Sentiment, and Who Is Affected Most? A Longitudinal Analysis of the UK and Japan Cases’. Comparative Migration Studies 9 (1): 24. https://doi.org/10.1186/s40878-021-00231-7.

Rudolph, Lukas, and Markus Wagner. 2021. ‘Europe’s Migration Crisis: Local Contact and Out‐group Hostility’. European Journal of Political Research, May, 1475-6765.12455. https://doi.org/10.1111/1475-6765.12455.

Ruedin, Didier. 2019. ‘Attitudes to Immigrants in South Africa: Personality and Vulnerability’. Journal of Ethnic and Migration Studies 45 (7): 1108–26. https://doi.org/10.1080/1369183X.2018.1428086.

Schaub, Max, Johanna Gereke, and Delia Baldassarri. 2020. ‘Strangers in Hostile Lands: Exposure to Refugees and Right-Wing Support in Germany’s Eastern Regions’. Comparative Political Studies, September, 001041402095767. https://doi.org/10.1177/0010414020957675.

Steinmayr, Andreas. 2020. ‘Contact versus Exposure: Refugee Presence and Voting for the Far-Right’. The Review of Economics and Statistics, May, 1–47. https://doi.org/10.1162/rest_a_00922.

Zschirnt, Eva, and Didier Ruedin. 2016. ‘Ethnic Discrimination in Hiring Decisions: A Meta-Analysis of Correspondence Tests 1990–2015’. Journal of Ethnic and Migration Studies 42 (7): 1115–34. https://doi.org/10.1080/1369183X.2015.1133279.

p-hacking: try it yourself!

It’s not new, but it’s still worth sharing:

The instructions go: “You’re a social scientist with a hunch: The U.S. economy is affected by whether Republicans or Democrats are in office. Try to show that a connection exists, using real data going back to 1948. For your results to be publishable in an academic journal, you’ll need to prove that they are “statistically significant” by achieving a low enough p-value.”

The tool is here: https://projects.fivethirtyeight.com/p-hacking/

And more on p-hacking here: Wikipedia — to understand why “success” in the above is not what it seems.

Excellence by Nonsense

I know it’s 5 years old, but I still think this description of academia deserves a wider audience.

In this chapter, Binswanger (a critic of the current scientific process) explains how artificially staged competitions affect science and how they result in nonsense. An economist himself, Binswanger provides examples from his field and shows how impact factors and publication pressure reduce the quality of scientific publications. Some might know his work and arguments from his book ‘Sinnlose Wettbewerbe’.

Binswanger, Mathias. 2014. ‘Excellence by Nonsense: The Competition for Publications in Modern Science’. In Opening Science: The Evolving Guide on How the Internet Is Changing Research, Collaboration and Scholarly Publishing, edited by Sönke Bartling and Sascha Friesike, 49–72. New York: Springer. https://doi.org/10.1007/978-3-319-00026-8_3. [open access]