BC SOCIETY
Saved by @CodyyyGardner

I want to add an addendum to this thread from the other day to show why publishing an n=1 is so bad. It's because I can likely identify and put a name to this student.

(I'm not going to do that here but I am going to show you how easy it is.)

 To do that, let's talk about the IPEDS data set. IPEDS is a US database that contains a range of information about US universities, such as enrollment, test scores, graduation rates, etc.

One notable data table shows graduated students by major and ethnicity.
(FYI, here is the IPEDS data: https://t.co/K4OwsyLLsE It's an open database so you can explore at your leisure.)
Back to the "Completions" table which shows ethnicity by major. This happens to line up with the n=1 from the offending article which identified a student by their ethnicity and major.
Sorry, the n=1 was year in school and ethnicity but I've now used the IPEDS data to find out their major. Linking datasets on minority populations is very very powerful.
At this point, I can do a little digging either by department "happy graduation" announcements or even the graduation program to winnow the list down to all of the majors for that degree for the year. Now it's a matter of figuring out which minority student is the n=1.
This may not be foolproof to get me the exact name of the student, but using only publicly available data I've gotten *really* close to identifying them.
Again, I'm not actually doing this to identify that n=1 student but rather I want to show you how easily it can be done. Give me 30 minutes and I will have a list of potential names, and if one name is not "white" I might actually have THE name.
This is the danger of publishing n=1. I CAN IDENTIFY THAT STUDENT FROM PUBLICLY AVAILABLE DATA. It's not that hard.
All that tells me is that they were in the study, not actually what they did at the library.

BUT it also tells me that you don't care enough to protect the identities of your minority students.
And it tells me that the journal and peer reviewers don't know enough about de-identification and re-identification to prevent a paper with n=1 to be published.
So if you were curious in my thread from the other day about why n=1 is so bad, this is why. I have enough information to identify a student in the study by name.
I would really really like this to be the last time I have to write a thread like this or do an exercise to potentially identify a student who in NO WAY deserves to be identified just because they use the library.
So let's never published n=1 ever again. Basically, if your n's are under 10, you should see a red flag and either group small n's together or obscure the data in some way (e.g. "n<20").
I'll end with the fact that none of this is okay. In any field but especially in a field that is supposed to protect patron's privacy. We need to do better. All of us. Researchers, writers, reviewers, editors, etc. to make sure this doesn't happen again.
And finally, thanks to @hedgielib and @IandPangurBan who suggested I add this information on exactly how easy it is to identify someone by name in a published example of n=1.

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2/ Dr. King delivered his speech in what he called the “hallowed spot” of the Lincoln memorial. More than a picturesque backdrop, King relied on America’s reverence for Lincoln and the common celebration of Lincoln’s words and acts, especially the Emancipation Proclamation.


3/ “Five score years ago…” King echoes the words of the Gettysburg Address. Like Lincoln, King invokes the Declaration of Independence and the fundamental principles of American freedom, “life, liberty and the pursuit of happiness.” Every person in that crowd knew these words.


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global health policy in 2020 has centered around NPI's (non-pharmaceutical interventions) like distancing, masks, school closures

these have been sold as a way to stop infection as though this were science.

this was never true and that fact was known and knowable.

let's look.


above is the plot of social restriction and NPI vs total death per million. there is 0 R2. this means that the variables play no role in explaining one another.

we can see this same relationship between NPI and all cause deaths.

this is devastating to the case for NPI.


clearly, correlation is not proof of causality, but a total lack of correlation IS proof that there was no material causality.

barring massive and implausible coincidence, it's essentially impossible to cause something and not correlate to it, especially 51 times.

this would seem to pose some very serious questions for those claiming that lockdowns work, those basing policy upon them, and those claiming this is the side of science.

there is no science here nor any data. this is the febrile imaginings of discredited modelers.

this has been clear and obvious from all over the world since the beginning and had been proven so clearly by may that it's hard to imagine anyone who is actually conversant with the data still believing in these responses.

everyone got the same R
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Hi @officestudents @EHRC @EHRCChair @KishwerFalkner @RJHilsenrath @trussliz @GEOgovuk

The Equality and Diversity section of your job application has 'gender' in what appears to be a list of the protected characteristics under the Equality Act 2010.

However...

1/15


However, 'gender' is not a protected characteristic under the Equality Act 2010 and is not defined in the Act.

https://t.co/qisFhCiV1u

Sex is the protected characteristic under the Act, but that is not on your list.

2/15


You then ask for the 'gender' of the applicant with options:

Male
Female.

3/15


Again, 'gender' is not a protected characteristic under the Equality Act 2010 and is not defined in the Act.

https://t.co/qisFhCiV1u

4/15


Sex is the protected characteristic and the only two possible options for sex are 'Female' and 'Male' as defined in the Act and consistent with biology, but you don't ask for that.

https://t.co/CEJ0gkr6nF

'Gender' is not a synonym for sex.

5/15
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@AdamWJT @Greens4HS2 @TheGreenParty @GarethDennis @XRebellionUK @Hs2RebelRebel @HS2ltd Thirdly, we can estimate how much CO2 is generated by cutting down trees, and how much is captured by planting new trees. We can also estimate how much CO2 is needed to keep the railway running and generated by maintaining the track /4

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