Myth Busting, Plagiarism and Lie Detecting Big Data Criminology

In terms of myth busting long cherished science myths, Big Data technology led to the devastating new bombshell discovery that Darwin and Wallace committed the world's greatest science fraud by lies, plagiarism and glory theft of Patrick Matthew's prior-published theory, which their friends/editors/influencers and influencer's influencers read and cited and that Richard Dawkins never coined the term 'selfish gene' nor the concept that is now a biological meme, and so much more besides.

1. Nullius in Verba: Darwin's greatest Secret (Vol. 1 Paperback book).

2. The hi-tech detection of Darwin’s and Wallace’s possible science fraud: Big data criminology re-writes the history of contested discovery (Expert peer reviewed academic article).

3. On Knowledge Contamination: New Data Challenges Claims of Darwin’s and Wallace’s Independent Conceptions of Matthew’s Prior-Published Hypothesis (Expert peer reviewed science journal article).

4. Using Date Specific Searches on Google Books to Disconfirm Prior Origination Knowledge Claims for Particular Terms, Words, and Names (Expert peer reviewed social science journal article) .

This book @Viktor_MS @kncukier is essential reading for #Criminology.
Anyone exploring network theory or the benefits and potential dangers of predictive offending, victim algorithms and other #BigDataCriminology should read it. Here is my Amazon review. https://t.co/qeoJ4bSHnI pic.twitter.com/EKrueBk8FX

— Dr Mike Sutton (@Criminotweet) July 6, 2018

Top customer reviews

Dr Mike Sutton

5.0 out of 5 starsA Seminal Work

6 July 2018

Format: Paperback|Verified Purchase

This is a seminal work on Big Data. I have written on the topic myself with professor Mark Griffiths, who 
recommended the book to me.

Viktor Mayer-Schönberger and Kenneth Cukier, the authors of ‘Big Data’, do an excellent job of telling us exactly 
what Big Data is and how what we currently have is rooted in historic exercises in trying to make sure that as far as
is possible N=all. Of course, N (the sample) seldom does equal all, but what we call Big Data gets substantially 
nearer to that than purposively limited statistical sampling.

What I most like about this book is the fact it provides so many examples of how Big Data reveals insights that could
not have been obtained in any other way. But most importantly, the authors then explain the importance of the 
results obtained. For anyone interested, as am I, in social network theory then pages 30-31 are a must read.

The last chapters of the book reveal the limitations of Big Data and the inherent danger of relying upon predictive 
algorithms – particularly in the fields of criminology, policing and criminal justice.

I cannot recommend the book more highly. Buy it. Read it. Think about it. Be enlightened. It might well change the 
way you think about data.