The basis for the claim is that the proportion of mask-wearers who hate, resent or think badly of non-mask wearers (58%) is greater than the proportion of Remainers who think badly of Leavers (33%) (p. 11 of report) (2/n)
First problem with this: in order for something to be divisive, it's got to divide society, and the more evenly it divides society, the more divisive it is. But the (short) report doesn't show what % of the population wear a mask. (3/n)
To take it to extremes: if I found that the non-murderous part of the population held very negative views about the murderous part, much more negative than Remainers' views about Leavers, no-one would say that murder was a divisive issue (4/n)
Second, if an issue is divisive, it's probably got to be reciprocally divisive. The report doesn't say what non-mask-wearers think of mask-wearers. I think they probably just don't care (5/n)
It may be that these issues are addressed in the full survey results, but I can't seem to find these on the Demos website (6/6)
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In response to UCU's Marking and Assessment Boycott (MAB), my employer @RoyalHolloway has decided to implement emergency regulations which in my view seriously call into question the rigour of degrees awarded (1/7)
The regulations include allowing marks for a module to be scaled proportionately (you did 50% of the coursework; that counts for 100%) (2/7)
(cont.) to permit an unlimited number of "allow" outcomes for modules (previously used where sickness or other extenuating circumstances affected students' performance) (3/7)
Everyone knows the most fun way to watch the World Cup is to support the more democratic nation in each game. So here, thanks to @vdeminstitute data, is your group-by-group rundown! (1/n)
We start in Group A, where the Netherlands is clearly in pole position, and Qatar clearly in last place (2/n)
In Group B, England and Wales are in a dead heat (until and unless V-Dem produces estimates of sub-state democracy), with Iran placing last. (3/n)
MRP works by modelling responses as a function of different demographic and political characteristics, and then making predictions for different voter types (2/15)
It works well when responses can be accurately predicted by these characteristics, or when you have a stupidly large sample size (3/15)
Jeremy Pocklington said that ministers had ignored civil service advice concerning the £3.9bn Towns Fund, and had instead applied [ahem!] "their own qualitative assessment" (3/n)
Tonight I'll be presenting my #APSA2020 paper, "The voting power of demographic groups". You can find the paper at drive.google.com/file/d/1G-SHiy… and a video presentation at (1/n)
The idea behind the paper is simple: the power of a voter group is the number of seats where the result would have been different had that voter group not voted—or alternately, the number of seats in which that voter group was pivotal (2/n)
The implementation is also simple, in a sense, and relies on multilevel regression and post-stratification (MRP) plus some post-hoc adjustments (3/n)