We've been forced to pay drastic economic and social costs to battle the existential threat COVID-19 is perceived to be. It has cost the world trillions of dollars and the loss of myriad civil liberties, so it makes sense for societies to ensure the price paid was worth it and if not, to demand extreme accountability in return.
Fact is, COVID-19 was destined to be Fake News from the start, the question was how large a fake would it prove to be? Given the coronaviruses' "novel" designation, early COVID-19 models must have been heavy on assumptions and light on factual data. They could only spit out wild guesses and, as it turns out, they guessed poorly. Regardless, the guesses proved more than good enough for government work.
Calculating The COVID-19 Delusion Gap
Now, as the world collects, corrects (hopefully), and collates actual data, we can use those factual statistics to calculate the scale of COVID delusion that triggered our political leaders into unleashing their inner tyrants.
Do you recall how COVID-19 models predicted over 2.2 million US deaths? Notice that the media now focuses on other factors, such as number of infections or second waves or masks? Yes, they promoted the (inflated) 100,000 US death milestone but they took pains not to relate that number to the original predictions, the "knowledge" used to justify totalitarian policies.
The ratio between predicted and actual deaths represents the gap between (model) perception and reality, otherwise known as delusion. As of June 28, worldometers has the US death total at 128,211. [https://www.worldometers.info/coronavirus/#countries]. Accepting that number at face value, the current COVID-19 delusion gap factor is 17.16, a staggering level of delusion to be basing important judgments on.
That $50,000 car you've been eying is now on sale for $858,000. Same car plus the COVID delusion factor makes it a no-go no-brainer. Yet, that's the brain our leaders were on when they made their big decisions to destroy your job, quarantine your healthy body, and borrow trillions to cover your losses. Trillions you, not they, have to pay back. Sadly, they must love that brain because they're still on it!
Fake News is expensive, dangerous, and sadly, extremely effective when people trust the source or the message resonates with current beliefs, which were created with Fake News. Without an unbiased method of identifying Fake News, resolving "truth" becomes a fact checking competition about whose facts matter. Those arguments are doomed to fail in bitter partisanship because they are grounded in deeply rooted ideologies rather than factual realities.
The good news is there is an unbiased Bias And Deceit Analysis methodology that can not only identify Fake News, but quantify just how fake.
Fake News Debugging: An Introduction
If we are going to identify Fake News in an unbiased manner, we first need an unbiased definition of it.
Fake News is the successful dissemination of "Fake Information", which, simply put, is information that causes perceptions to detach from reality.
With that unbiased, generic definition to guide us, let's take a look at one possible way of eliminating bias from our Fake News analysis methodology.
From Reading Comprehension To Perceptual Analysis
Similarities between computers and the human mind have long been noted and that analogy plays a critical role in this unique analysis of Fake News. If we view our emotional and logical brains as information processors, then it makes sense to treat news like a source of data for those processors.
For a software developer, data integrity is much more important than data content. Without data integrity, the content cannot be trusted to deliver useful and accurate information to those who rely on it. Data integrity means the data properly fits the software/hardware consuming it because when it doesn't, it will misbehave or worse, break the software. Similarly, information that disrupts our perceptual process does not fit our brains. It lacks perceptual integrity, which breaks our thinking into crapping out delusional perceptions and fake beliefs.
Adopting this software developer perspective changes the purpose of the analysis from assessing the meaning of the content to validating the quality, or perceptual integrity, of the content. The goal is to test the trustworthiness of information. Can it, and by extension, the authors and institutions involved, be trusted with your mind?
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Fake News is intellectual junk-food, consume at everybody's risk
The shift in perspective has the additional benefit of eliminating ideological bias from the analysis because bias is embedded in content (where fact checkers live). This approach sets the foundation for a unique, non-partisan, ideologically independent, source independent, technical analysis that can identify fake information and reveal inherent biases. A Fake News killer.
In essence, the analysis serves as a data validator (to be explained at a later date) and would be utilized to identify fake information (intellectual poison) prior to consuming it for comprehension. And therein lies its biggest drawback: time and effort. Information analysis is very time intensive and what is a short 5-minute read may take an hour or two to fully analyse and few, if any, are willing to spend that time. We want information and we want it now, which is why we're so vulnerable to Fake News delusion.
Delusional decisions are a bad idea, and the deeper the delusion the worse the cost. From a developer's perspective, bad results are a clue there is a bug in the system. The enormous financial and social costs of COVID-19 policies are the clue there is a bug in our news information system. The fact the media insist we continue to live with COVID coercions is the clue that their intentions are deliberate. They are demanding we continue to lose more wealth and freedom. Nice.
Fake News is intellectual junk-food, consume at everybody's risk.
Mark Gray hails from the Kirkland Lake, Ontario area and has spent over 30 years as an Analyst/Developer in Big IT, mostly in Calgary’s Oil-And-Gas Sector. Creator of an non-partisan, analytical methodology that seeks out and identifies Bias and Deceit embedded in weaponized information.
We've been forced to pay drastic economic and social costs to battle the existential threat COVID-19 is perceived to be. It has cost the world trillions of dollars and the loss of myriad civil liberties, so it makes sense for societies to ensure the price paid was worth it and if not, to demand extreme accountability in return.
Fact is, COVID-19 was destined to be Fake News from the start, the question was how large a fake would it prove to be? Given the coronaviruses' "novel" designation, early COVID-19 models must have been heavy on assumptions and light on factual data. They could only spit out wild guesses and, as it turns out, they guessed poorly. Regardless, the guesses proved more than good enough for government work. 
