COINTELPRO was a real program used by the US Gov. It was a series of covert and illegal projects conducted by the FBI aimed at surveilling, infiltrating, discrediting, and disrupting domestic American political organizations.
It is no conspiracy theory. Look it up.
More from Blue Canaries
Austin, Texas:
Connecting the Dots
On Dec 15, I created a thread which showed that the City of Austin did in fact have a contract w Solarwinds which was procured through Insight Public Sector. SolarWinds hack linked toRussian group — APT29 aka Cozy
Connecting the Dots
On Dec 15, I created a thread which showed that the City of Austin did in fact have a contract w Solarwinds which was procured through Insight Public Sector. SolarWinds hack linked toRussian group — APT29 aka Cozy
1. CITY of AUSTIN :
— Blue Canaries (@CanariesBlue) December 15, 2020
You've been HACKED!
In Apr. 2019, the Austin City Council approved a contract w Insight Public Sector., a procurement company, in order to indirectly hire SolarWinds. pic.twitter.com/2v9cGgkr0F
More from Government
Which metric is a better predictor of the severity of the fall surge in US states?
1) Margin of Democrat victory in Nov 2020 election
or
2) % infected through Sep 1, 2020
Can you guess which plot is which?
The left plot is based on the % infected through Sep 1, 2020. You can see that there is very little correlation with the % infected since Sep 1.
However, there is a *strong* correlation when using the margin of Biden's victory (right).
Infections % from https://t.co/WcXlfxv3Ah.
This is the strongest single variable I've seen in being able to explain the severity of this most recent wave in each state.
Not past infections / existing immunity, population density, racial makeup, latitude / weather / humidity, etc.
But political lean.
One can argue that states that lean Democrat are more likely to implement restrictions/mandates.
This is valid, so we test this by using the Government Stringency Index made by @UniofOxford.
We also see a correlation, but it's weaker (R^2=0.36 vs 0.50).
https://t.co/BxBBKwW6ta
To avoid look-ahead bias/confounding variables, here is the same analysis but using 2016 margin of victory as the predictor. Similar results.
This basically says that 2016 election results is a better predictor of the severity of the fall wave than intervention levels in 2020!
1) Margin of Democrat victory in Nov 2020 election
or
2) % infected through Sep 1, 2020
Can you guess which plot is which?
The left plot is based on the % infected through Sep 1, 2020. You can see that there is very little correlation with the % infected since Sep 1.
However, there is a *strong* correlation when using the margin of Biden's victory (right).
Infections % from https://t.co/WcXlfxv3Ah.
This is the strongest single variable I've seen in being able to explain the severity of this most recent wave in each state.
Not past infections / existing immunity, population density, racial makeup, latitude / weather / humidity, etc.
But political lean.
One can argue that states that lean Democrat are more likely to implement restrictions/mandates.
This is valid, so we test this by using the Government Stringency Index made by @UniofOxford.
We also see a correlation, but it's weaker (R^2=0.36 vs 0.50).
https://t.co/BxBBKwW6ta
To avoid look-ahead bias/confounding variables, here is the same analysis but using 2016 margin of victory as the predictor. Similar results.
This basically says that 2016 election results is a better predictor of the severity of the fall wave than intervention levels in 2020!