Exploring a bit of the magic source, neuroliberalism and what data does and doesn’t know about me

In this weeks HEWN newsletter (no AI or algorithms there, just good old human research, editing, evaluation and critique) Audrey Waters said:

“If there is one article I would insist those in education / technology read this weekend, it’s this one by Ben Williamson: “Learning from psychographic personality profiling.” Really. Read it.”

I did  – so should you.

I’ve never been a fan of any kind of personality profiling or psychometric testing. In one of my previous jobs we were were subjected to psychometric testing as part of team building days. I hated it.  It didn’t serve any purpose that I could see – and it was all done on paper which I think  was destroyed. However I am aware that even back then it was used more regularly and rigorously by many companies to sort, select and manage employees.

As the Cambridge Analytica Facebook data scandal has shown it is now being used as the basis of digital profiling.  If you’re worried at all about data driving personalised learning, the sinister sausage machine of education,  then we all need to be looking to the work of people like Ben.  How this type of data profiling is and will be sold to education is a major concern. Particularly if we really want to allow higher education to be inclusive to be able to help with widening participation and address the attainment gaps that certainly here in Scotland sadly seem to be growing every year.  I had never heard the term “neuroliberalism” before reading the Ben’s post but it could now be my favourite new word.

Following my post yesterday, and reading Ben’s post I decided to have a closer look at my own data using a bit of data magic from  Magic Source,  This “service” developed and run by the University of Cambridge is

A personalisation engine that accurately predicts psychological traits from digital footprints of human behaviour

Using your Facebook and or Twitter data it will predict:

your psycho-demographic profile from digital footprints of your behaviour. It reveals how you might be perceived by others online and provides detailed insights on your personality, intelligence, life satisfaction and more.

Predictions are based on opt-in psychological ground truth from over 6 million volunteers, and our data has been used in over 45 peer-reviewed scientific articles.

So what did it make of me?  Well,  from my twitter data it deduced that I am 33 (FTW!) but it was not so sure about my gender.

In terms of the “Big 5” personality traits I am kind of artistic and liberal but a bit of a loner.

Going into a bit more depth it would appear that I am quite open to “things”

and my Jungian personality is INJT – introverted, intuitive, thinking,  judging. I’m also totally average.

So what about Facebook? Well still not sure but thought I was morel likely to be female.

In terms of the Big 5 some subtle differences from Twitter – but basically the same.

However the more interesting thing about the Facebook data is that “the magic” tells you what like make you appear more/ less impulsive and more/less artistic and liberal

I’m also, according this limited data profile, I’m about averagely intelligent and could be happier,  but there is “still a chance that I might be brighter than the average person”

Again quite interesting to see what likes make me appear more or less intelligent.


My Jungain personality type this time around is ISTJ  – introverted, sensitive, thinking judging.

But probably more interesting is the political and religious inferences this data magic produces.

And the likes and dislikes it basis this on.

It also seems to think I am a nurse . ..

So what does it all mean? Should I be relived that this isn’t that accurate, that I should just stop liking anything related to shopping to make me appear more intelligent? Should I start liking more sites and posts that make me appear more liberal and artistic Should I just carry on regardless?  Should I be worried that my actual self may be disregarded, not given an opportunity to get a new job based on this data?

What I should be worried about is what Ben says

Expert knowledge about students is increasingly being mediated through an edu-data analytics industry, which is bringing new powers to see into the hidden and submerged depths of students’ cognition, brains and emotions, while also allowing ed-tech companies and policymakers to act ‘smarter’, in real-time and predictively, to intervene in and shape students’ futures.

When will that be applied to staff and what measures will be applied to us? How critical will we be allowed to be? What will the neuroliberal indicators of staff suitability be?

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