upon launching an investigation into Title IX complaints against him alleging sexual harassment. Fryer alleged that he was "unfairly scrutinized ... for his skin color." Harvard confirmed that its Office for Dispute Resolution received complaints against Fryer in January, March, and April 2018.
In July 2019, Fryer was suspended from the Harvard faculty for two years without pay, a disciplinary action determined by a panel of tenured faculty. Harvard also determined that, after returning from suspension, Fryer cannot be an adviser or supervisor, have access to graduate fellows, or teach graduate workshops, but can teach graduate classes. Fryer had been one of Harvard's most highly paid professors. As the sanctions took effect, Harvard permanently closed EdLabs in September 2019.
Also, economics is a social science and not considered a hard science. When you claim only hard science is acceptable, then utilize the research from someone outside of a hard science, it weakens your argument. It becomes very circular in logic.
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In 2019 there were a total of 39 unarmed blacks and whites killed by police. 25 were white and 14 were black. So 64 percent were white and 36 percent were black. However , blacks between the ages of 10-34 die from all types of homicide at 13 times the rate of whites. Black victims, armed and unarmed, account for 23 percent of all police shootings, and are 13 percent of the national population. They are about 7300 black shooting victims a year; the 14 unarmed blacks shot by police would be about .02 percent of that total. Interestingly, the number of unarmed black shooting victims is down 63 percent since 2015.
Economics is the closest thing to hard science that exists in the social sciences.
It's real science because it predict, not just describe, for the most part. It doesn't make the type of claims being made in the social pseudosciences that these sociologists do, nor will you ever see a hard scientist with integrity make these misleading claims to promulgate false narratives.
It's real science because it predict, not just describe, for the most part. It doesn't make the type of claims being made in the social pseudosciences that these sociologists do, nor will you ever see a hard scientist with integrity make these misleading claims to promulgate false narratives.
So now the bar as changed to your interpretation of it being a soft science, but what you feel is closer to a hard science? I mean you tried to bash soft science and social science pretty hard earlier, and now you seem fine with them. Seems a bit circular to me.
The 'scientific method' which characterises' hard sciences is simply impossible in economics, theoretically and practically. Physics, the most directly comparable hard science, is characterised by 'truth' and by hard rules. Economics by its nature studies humans and ultimately human nature is determined by things far beyond our understanding and ability to model mathematically or analytically. We can make a 'best guess' and get close but there is no comprehensive truth in economics. Everything is a heuristic or a rule of thumb rather than an absolute truth.
Practically, its difficult to apply the scientific method of experimentation to economics. Generally, where a physicist can conduct a controlled experiment or a chemist can mix two elements, if I want to test how unemployment affects wages I can't (ethically) run an experiment. What I can do is look at data and try and manipulate it in a way that gives the correct result (and by correct I mean the true result, not necessarily the one I want!) Attempts to run experiments in economics are possible, important but ultimately limited. We sometimes have people play lab games to test the validity of economic theory but we have to be careful that we understand why players are doing what they are. We only observe actions in economics. Not motivations. Particles don't have motivations, but humans do, and what they are will affect how they act under different rules.
On a slightly more technical level, the 'economic method' is fundamentally different, although this is a highly esoteric point. I'm making it because some people try to associate economics with physics because of the overwhelming use of mathematics. This is wrong, because their use is fundamentally different.
Hard sciences use mathematics and statistics to measure and explain natural phenomena. Economists use it to analyse. Its called 'axiomatic mathematics' and what it means is that I start with a set of assumptions (people maximise their happiness, people have a fixed income, there are N goods they can choose from etc) and then I figure out the logical conclusion of those assumptions. That is something economics does all the time which is pretty exclusive and it is because it is seeking to explain something which is practically unexplainable, and so we have to strictly define the circumstances under which our theory will hold.
Apologies if this was too long or technical, I'm happy to clarify (or debate) any of it. The tl;dr is that economics is not a hard science because it is explaining something so fundamentally different which requires the methods used to be fundamentally different is superficially similar.
The 'scientific method' which characterises' hard sciences is simply impossible in economics, theoretically and practically. Physics, the most directly comparable hard science, is characterised by 'truth' and by hard rules. Economics by its nature studies humans and ultimately human nature is determined by things far beyond our understanding and ability to model mathematically or analytically. We can make a 'best guess' and get close but there is no comprehensive truth in economics. Everything is a heuristic or a rule of thumb rather than an absolute truth.
Practically, its difficult to apply the scientific method of experimentation to economics. Generally, where a physicist can conduct a controlled experiment or a chemist can mix two elements, if I want to test how unemployment affects wages I can't (ethically) run an experiment. What I can do is look at data and try and manipulate it in a way that gives the correct result (and by correct I mean the true result, not necessarily the one I want!) Attempts to run experiments in economics are possible, important but ultimately limited. We sometimes have people play lab games to test the validity of economic theory but we have to be careful that we understand why players are doing what they are. We only observe actions in economics. Not motivations. Particles don't have motivations, but humans do, and what they are will affect how they act under different rules.
On a slightly more technical level, the 'economic method' is fundamentally different, although this is a highly esoteric point. I'm making it because some people try to associate economics with physics because of the overwhelming use of mathematics. This is wrong, because their use is fundamentally different.
Hard sciences use mathematics and statistics to measure and explain natural phenomena. Economists use it to analyse. Its called 'axiomatic mathematics' and what it means is that I start with a set of assumptions (people maximise their happiness, people have a fixed income, there are N goods they can choose from etc) and then I figure out the logical conclusion of those assumptions. That is something economics does all the time which is pretty exclusive and it is because it is seeking to explain something which is practically unexplainable, and so we have to strictly define the circumstances under which our theory will hold.
Apologies if this was too long or technical, I'm happy to clarify (or debate) any of it. The tl;dr is that economics is not a hard science because it is explaining something so fundamentally different which requires the methods used to be fundamentally different is superficially similar.
Several scholars have rightly pointed out that these data all begin with an interaction, and suggested that racist policing manifests itself in more interactions between blacks and the police. The impact of this hypothesis in our shootings data seems minimal. The results on police shootings are statistically the same across all call types—ranging from officer-initiated contact with a suspicious person (where racism in whom to police is likely paramount) to a 911 call of a homicide in progress (where interaction with the potential suspect is more likely independent of race).
Are the data nationally representative? We don’t know. But at least two other studies, both published in 2016—by Phillip Atiba Goff et al. and Ted R. Miller et al.—have since found the same using different data.
Are the data nationally representative? We don’t know. But at least two other studies, both published in 2016—by Phillip Atiba Goff et al. and Ted R. Miller et al.—have since found the same using different data.
2020 update: The specific flaws of Roland Fryer's paper have now been characterized in two studies (by other scholars, not myself). Knox, Lowe, and Mummolo (2019) reanalyze Fryer's data to find it understates racial biases. Ross, Winterhalder, and McElreath (2018) do something similar through a statistical simulation.
Fryer’s analysis is highly flawed, however. It suffers from major theoretical and methodological errors, and he has communicated the results to news media in a way that is misleading. While there have long been problems with the quality of police shootings data, there is still plenty of evidence to support a pattern of systematic, racially discriminatory use of force against black people in the United States.
Breaking down the analysis of police shootings in Houston
There should be no argument that black and Latino people in Houston are much more likely to be shot by police compared to whites. I looked at the same Houston police shooting dataset as Fryer for the years 2005-2015, which I supplemented with census data, and found that black people were over 5 times as likely to be shot relative to whites. Latinos were roughly twice as likely to be shot versus whites.
Fryer was not comparing rates of police shootings by race, however. Instead, his research asked whether these racial differences were the result of “racial bias” rather than merely “statistical discrimination”. Both terms have specific meanings in economics. Statistical discrimination occurs when an individual or institution treats people differently based on racial stereotypes that ‘truly’ reflect the average behavior of a racial group. For instance, if a city’s black drivers are 50% more likely to possess drugs than white drivers, and police officers are 50% more likely to pull over black drivers, economic theory would hold that this discriminatory policing is rational. If, however, police were to pull over black drivers at a rate that disproportionately exceeded their likelihood of drug possession, that would be an irrational behavior representing individual or institutional bias.
Once explained, it is possible to find the idea of “statistical discrimination” just as abhorrent as “racial bias”. One could point out that the drug laws police enforce were passed with racially discriminatory intent, that collectively punishing black people based on “average behavior” is wrong, or that – as a self-fulfilling prophecy – bias can turn into statistical discrimination (if black people’s cars are searched more thoroughly, for instance, it will appear that their rates of drug possession are higher). At the same time, studies assessing the extent of racial bias above and beyond statistical discrimination have been able to secure legal victories for civil rights. An analysis of stop-and-frisk data by Jeffrey Fagan, which found evidence racial bias, was an important part of the court case against the NYPD, and helped secure an injunction against the policy.
Even if one accepts the logic of statistical discrimination versus racial bias, it is an inappropriate choice for a study of police shootings. The method that Fryer employs has, for the most part, been used to study traffic stops and stop-and-frisk practices. In those cases, economic theory holds that police want to maximize the number of arrests for the possession of contraband (such as drugs or weapons) while expending the fewest resources. If they are acting in the most cost-efficient, rational manner, the officers may use racial stereotypes to increase the arrest rate per stop. This theory completely falls apart for police shootings, however, because officers are not trying to rationally maximize the number of shootings. The theory that is supposed to be informing Fryer's choice of methods is therefore not applicable to this case. He seems somewhat aware of this issue. In his interview with the New York Times, he attributes his ‘surprising’ finding to an issue of “costs, legal and psychological” that happen following a shooting. In what is perhaps a case of cognitive dissonance, he seems to not have reflected on whether the question of cost renders his choice of methods invalid.
Economic theory aside, there is an even more fundamental problem with the Houston police shooting analysis. In a typical study, a researcher will start with a previously defined population where each individual is at risk of a particular outcome. For instance, a population of drivers stopped by police can have one of two outcomes: they can be arrested, or they can be sent on their way. Instead of following this standard approach, Fryer constructs a fictitious population of people who are shot by police and people who are arrested. The problem here is that these two groups (those shot and those arrested) are, in all likelihood, systematically different from one another in ways that cannot be controlled for statistically (UPenn Professor Uri Simonsohn expands on this point here). Fryer acknowledges this limitation in a brief footnote, but understates just how problematic it is. Properly interpreted, the actual result from Fryer’s analysis is that the racial disparity in arrest rates is larger than the racial disparity in police shootings. This is an unsurprising finding, and proves neither a lack of bias nor a lack of systematic discrimination.
Even if the difference in the arrest vs. shooting groups could be accounted for, Fryer tries to control for these differences using variables in police reports, such as if the suspect was described as 'violently resisting arrest'. There is reason to believe that these police reports themselves are racially biased. An investigation of people charged with assaulting a police officer in Washington, DC found that this charge was applied disproportionately towards black residents even for situations in which no assault actually occurred. This was partly due to an overly broad definition of assault against police in DC law, but the principle - that police are likely to describe black civilians as more threatening - is applicable to other jurisdictions.
I’ll also briefly note that there was another analysis, using data from multiple cities, that looked at racial differences in whether or not civilians attacked officers before they were shot. Fryer himself downplays the credibility of this analysis, because it relied on reports from police who had every incentive to misrepresent the order of events.
Racial inequality in police shootings
Fryer’s study is far from the first to investigate racial bias or discrimination in police shootings. A number of studies have placed officers in shooting simulators, and most have shown a greater propensity for shooting black civilians relative to whites. Other research has found that cities with black mayors and city councilors have lower rates of police shootings than would otherwise be expected. A recent analysis of national data showed wide variation in racial disparities for police shooting rates between counties, and these differences were not associated with racial differences in crime rates. This is just a small sample of the dozens of studies on police killings published since the 1950s, most of which suggests that racial bias is indeed a problem.
It is a failure of journalism that the New York Times heavily promoted this study without seeking critical perspectives from experts in the field. Fryer makes basic methodological errors, overstates the quality of his results, and casually uses the term “racial bias” in a way that is nearly guaranteed to be misinterpreted by anyone who isn’t an economist.
Fryer’s analysis is highly flawed, however. It suffers from major theoretical and methodological errors, and he has communicated the results to news media in a way that is misleading. While there have long been problems with the quality of police shootings data, there is still plenty of evidence to support a pattern of systematic, racially discriminatory use of force against black people in the United States.
Breaking down the analysis of police shootings in Houston
There should be no argument that black and Latino people in Houston are much more likely to be shot by police compared to whites. I looked at the same Houston police shooting dataset as Fryer for the years 2005-2015, which I supplemented with census data, and found that black people were over 5 times as likely to be shot relative to whites. Latinos were roughly twice as likely to be shot versus whites.
Fryer was not comparing rates of police shootings by race, however. Instead, his research asked whether these racial differences were the result of “racial bias” rather than merely “statistical discrimination”. Both terms have specific meanings in economics. Statistical discrimination occurs when an individual or institution treats people differently based on racial stereotypes that ‘truly’ reflect the average behavior of a racial group. For instance, if a city’s black drivers are 50% more likely to possess drugs than white drivers, and police officers are 50% more likely to pull over black drivers, economic theory would hold that this discriminatory policing is rational. If, however, police were to pull over black drivers at a rate that disproportionately exceeded their likelihood of drug possession, that would be an irrational behavior representing individual or institutional bias.
Once explained, it is possible to find the idea of “statistical discrimination” just as abhorrent as “racial bias”. One could point out that the drug laws police enforce were passed with racially discriminatory intent, that collectively punishing black people based on “average behavior” is wrong, or that – as a self-fulfilling prophecy – bias can turn into statistical discrimination (if black people’s cars are searched more thoroughly, for instance, it will appear that their rates of drug possession are higher). At the same time, studies assessing the extent of racial bias above and beyond statistical discrimination have been able to secure legal victories for civil rights. An analysis of stop-and-frisk data by Jeffrey Fagan, which found evidence racial bias, was an important part of the court case against the NYPD, and helped secure an injunction against the policy.
Even if one accepts the logic of statistical discrimination versus racial bias, it is an inappropriate choice for a study of police shootings. The method that Fryer employs has, for the most part, been used to study traffic stops and stop-and-frisk practices. In those cases, economic theory holds that police want to maximize the number of arrests for the possession of contraband (such as drugs or weapons) while expending the fewest resources. If they are acting in the most cost-efficient, rational manner, the officers may use racial stereotypes to increase the arrest rate per stop. This theory completely falls apart for police shootings, however, because officers are not trying to rationally maximize the number of shootings. The theory that is supposed to be informing Fryer's choice of methods is therefore not applicable to this case. He seems somewhat aware of this issue. In his interview with the New York Times, he attributes his ‘surprising’ finding to an issue of “costs, legal and psychological” that happen following a shooting. In what is perhaps a case of cognitive dissonance, he seems to not have reflected on whether the question of cost renders his choice of methods invalid.
Economic theory aside, there is an even more fundamental problem with the Houston police shooting analysis. In a typical study, a researcher will start with a previously defined population where each individual is at risk of a particular outcome. For instance, a population of drivers stopped by police can have one of two outcomes: they can be arrested, or they can be sent on their way. Instead of following this standard approach, Fryer constructs a fictitious population of people who are shot by police and people who are arrested. The problem here is that these two groups (those shot and those arrested) are, in all likelihood, systematically different from one another in ways that cannot be controlled for statistically (UPenn Professor Uri Simonsohn expands on this point here). Fryer acknowledges this limitation in a brief footnote, but understates just how problematic it is. Properly interpreted, the actual result from Fryer’s analysis is that the racial disparity in arrest rates is larger than the racial disparity in police shootings. This is an unsurprising finding, and proves neither a lack of bias nor a lack of systematic discrimination.
Even if the difference in the arrest vs. shooting groups could be accounted for, Fryer tries to control for these differences using variables in police reports, such as if the suspect was described as 'violently resisting arrest'. There is reason to believe that these police reports themselves are racially biased. An investigation of people charged with assaulting a police officer in Washington, DC found that this charge was applied disproportionately towards black residents even for situations in which no assault actually occurred. This was partly due to an overly broad definition of assault against police in DC law, but the principle - that police are likely to describe black civilians as more threatening - is applicable to other jurisdictions.
I’ll also briefly note that there was another analysis, using data from multiple cities, that looked at racial differences in whether or not civilians attacked officers before they were shot. Fryer himself downplays the credibility of this analysis, because it relied on reports from police who had every incentive to misrepresent the order of events.
Racial inequality in police shootings
Fryer’s study is far from the first to investigate racial bias or discrimination in police shootings. A number of studies have placed officers in shooting simulators, and most have shown a greater propensity for shooting black civilians relative to whites. Other research has found that cities with black mayors and city councilors have lower rates of police shootings than would otherwise be expected. A recent analysis of national data showed wide variation in racial disparities for police shooting rates between counties, and these differences were not associated with racial differences in crime rates. This is just a small sample of the dozens of studies on police killings published since the 1950s, most of which suggests that racial bias is indeed a problem.
It is a failure of journalism that the New York Times heavily promoted this study without seeking critical perspectives from experts in the field. Fryer makes basic methodological errors, overstates the quality of his results, and casually uses the term “racial bias” in a way that is nearly guaranteed to be misinterpreted by anyone who isn’t an economist.
This is a limitation on the data, but it's not the main criticism of Fryer.
The principle issue is that Fryer controls for the rate of interactions with the police, but that rate is effectively determined entirely by police officers (who have wide discretion in who to stop/pull over) and departmental policy (i.e. how many patrols get assigned to which neighborhoods). This means that we've already prevented ourselves from considering one major avenue where we have very solid evidence that racial bias is introduced, and, in particular, it means that the group "white people stopped by the police" is not identical to the group "black people stopped by the police:" black folks are substantially less likely to be engaged in criminal activity when they interact with the police. This article covers some of the methodological issues pretty well; if you're pressed for time you can scroll down to the infographic, which helps visualize some of the difficulty in this kind of analysis.
But the short version is this -- if the police are stopping more 'innocent' black folks (i.e. people with no contraband, weapons, or outstanding warrants on them at the time), but they're still being killed at a roughly equal rate to whites, this doesn't demonstrate an absence of bias at all. Quite to the contrary, it suggests that there is racial bias at play: there appears to be a lower threshold for killing black suspects than white ones.
As an analogy, imagine you're testing a new medication to prevent breast cancer, and you let doctors just recruit people for the study that they think could benefit from it. The doctors recommend that most of their female patients join their study, because breast cancer is a common problem for women, but they only recruit a handful of men who show some additional risk factor (e.g. a particular genetic marker). At the end of the study, you find that 0.1% of people who take the medication develop breast cancer within one year. Upon further examination of the data, you realize that this number is true across all groups: i.e. 0.1% of men who take the medication develop breast cancer, and 0.1% of women who take the medication develop breast cancer. Would you say that this medication is equally dangerous for all groups? If so, you're missing the fact that these two groups didn't have equal risks for breast cancer to begin with -- the drug could actually be lowering the risk for one group, while raising it for the other.
The principle issue is that Fryer controls for the rate of interactions with the police, but that rate is effectively determined entirely by police officers (who have wide discretion in who to stop/pull over) and departmental policy (i.e. how many patrols get assigned to which neighborhoods). This means that we've already prevented ourselves from considering one major avenue where we have very solid evidence that racial bias is introduced, and, in particular, it means that the group "white people stopped by the police" is not identical to the group "black people stopped by the police:" black folks are substantially less likely to be engaged in criminal activity when they interact with the police. This article covers some of the methodological issues pretty well; if you're pressed for time you can scroll down to the infographic, which helps visualize some of the difficulty in this kind of analysis.
But the short version is this -- if the police are stopping more 'innocent' black folks (i.e. people with no contraband, weapons, or outstanding warrants on them at the time), but they're still being killed at a roughly equal rate to whites, this doesn't demonstrate an absence of bias at all. Quite to the contrary, it suggests that there is racial bias at play: there appears to be a lower threshold for killing black suspects than white ones.
As an analogy, imagine you're testing a new medication to prevent breast cancer, and you let doctors just recruit people for the study that they think could benefit from it. The doctors recommend that most of their female patients join their study, because breast cancer is a common problem for women, but they only recruit a handful of men who show some additional risk factor (e.g. a particular genetic marker). At the end of the study, you find that 0.1% of people who take the medication develop breast cancer within one year. Upon further examination of the data, you realize that this number is true across all groups: i.e. 0.1% of men who take the medication develop breast cancer, and 0.1% of women who take the medication develop breast cancer. Would you say that this medication is equally dangerous for all groups? If so, you're missing the fact that these two groups didn't have equal risks for breast cancer to begin with -- the drug could actually be lowering the risk for one group, while raising it for the other.
Those statistics, however, cannot address the fundamental question: When a shooting might be justified by department standards, are police more likely actually to shoot if the civilian is black? Only our data can answer this question, because it contains information on situations in which a shooting might meet departmental standards but didn’t happen. The answer appears to be no.
I wonder if ole Travis Graf knew his one little tweet would turn into 14 pages of Internet message board bukkake?
force. To my dismay, this work has been widely misrepresented and misused by people on both sides of the ideological aisle.
The principle issue is that Fryer controls for the rate of interactions with the police, but that rate is effectively determined entirely by police officers (who have wide discretion in who to stop/pull over) and departmental policy (i.e. how many patrols get assigned to which neighborhoods). This means that we've already prevented ourselves from considering one major avenue where we have very solid evidence that racial bias is introduced, and, in particular, it means that the group "white people stopped by the police" is not identical to the group "black people stopped by the police:" black folks are substantially less likely to be engaged in criminal activity when they interact with the police. This article covers some of the methodological issues pretty well; if you're pressed for time you can scroll down to the infographic, which helps visualize some of the difficulty in this kind of analysis.
Fryer’s analysis is highly flawed, however. It suffers from major theoretical and methodological errors, and he has communicated the results to news media in a way that is misleading. While there have long been problems with the quality of police shootings data, there is still plenty of evidence to support a pattern of systematic, racially discriminatory use of force against black people in the United States.
Sam Harris podcast on police violence. Sam is about as left as possible but he is an honest intellectual. Worth the 25 minutes to hear it laid out intelligently.
This thread in a nutshell: LB is the professor in front of a class of 500 trying to explain why the earth is flat. 2 are listening, and 498 know he is out in left field.
No one is going to read a thesis like previously posted, because 1) they aren't the real world and 2) they aren't the real world.
No one is going to read a thesis like previously posted, because 1) they aren't the real world and 2) they aren't the real world.
I have never seen a person refuse data to this extent. But the streets are full of them right now. No amount of data or fact can alter their emotions.
They think the data are supporting their argument because they don’t understand it and/or are incompetent in statistical analysis, what conclusions are valid vs invalid.
This is why their argument is not based on reason, but appeals to emotion or other inconsequential rhetorical techniques, like shooting the messenger or simply posting incessantly.
It’s not just all worthless, but costs lives and destruction
This is why their argument is not based on reason, but appeals to emotion or other inconsequential rhetorical techniques, like shooting the messenger or simply posting incessantly.
It’s not just all worthless, but costs lives and destruction
They will try to convince you only they know the data and can define it for you. They claim they need to "treat" it first. That just means the need to manipulate it to fit their narrative.
They then work to discredit it as pseudo or soft. When they data they are utilizing is defined as soft.
They will promote widely discredited and dubunked research done by morally bankrupt individuals. Who themselves use race as an excuse for illegal and nefarious activities.
They promote views of " renowned scientists" who are currently serving 2 year suspensions for sexually harrassing women, in the work place and lying about it. For making racially discriminatory jokes.
Ask yourself why, if the data is so adundant amd irrefutable, the best support they can utilize to promote this disillusioned belief is with a widely discredited and proven deviant.
It's all done to gaslight you into a belief they need you to have to maintain control and the status quo.
They then work to discredit it as pseudo or soft. When they data they are utilizing is defined as soft.
They will promote widely discredited and dubunked research done by morally bankrupt individuals. Who themselves use race as an excuse for illegal and nefarious activities.
They promote views of " renowned scientists" who are currently serving 2 year suspensions for sexually harrassing women, in the work place and lying about it. For making racially discriminatory jokes.
Ask yourself why, if the data is so adundant amd irrefutable, the best support they can utilize to promote this disillusioned belief is with a widely discredited and proven deviant.
It's all done to gaslight you into a belief they need you to have to maintain control and the status quo.
If the streets are full, why is it only happening in 0.001% of American cities?
Just saying something proves a point without it ever actually doing so doesn't make it a reality.
Many on here have a far superior understanding of math, research and data than charlatans who feel a need to convince others of an unrealistic reality to maintain a sense of control and power. It's a very easy manipulation method to recognize.
Finally, analysis of police shooting data as a function of county-level predictors suggests that racial bias in police shootings is most likely to emerge in police departments in larger metropolitan counties with low median incomes and a sizable portion of black residents, especially when there is high financial inequality in that county.
That is because it's causal. Class is causal, crime density etc. @ganner918 Maybe he could process it, people like @d1ja and other blind partisans with no mathematical ability whatsoever want to sacrifice innocent lives for political gain.
And it's not without consequence.
And don't get the wrong idea, I still don't/won't read your postings other than like this, incidentally. Add d1ja to the list, I know your playbook and you are to be pitied... It's all just drivel.
That is because it's causal. Class is causal, crime density etc. @ganner918 Maybe he could process it, people like @d1ja and other blind partisans with no mathematical ability whatsoever want to sacrifice innocent lives for political gain.
And it's not without consequence.
And don't get the wrong idea, I still don't/won't read your postings other than like this, incidentally. Add d1ja to the list, I know your playbook and you are to be pitied... It's all just drivel.
Why is no one talking about the 2 white guys who ambushed, shot and killed 2 CA police officers?
Why do people always want to politicize the deaths of children for politicsl gain?