The Supreme Court ruled colleges and universities can no longer take race into consideration as a specific basis in admissions — a landmark decision that overturns long-standing precedent that has benefited Black and Latino students in higher education.
Questionable. In Northern Ireland, the police used to be something like 7% Catholic, policing a population that was over 40% Catholic. It was controversial at the time, but a 50:50 recruitment policy was put in place in the mid to late 90s, until the balance of the police force matched the wider community.
This is now very broadly accepted as a necessary and beneficial move. The current police force is generally seen as impartial (in terms of this one issue) while the old one was generally not trusted by the minority, to put it mildly.
I think it’s important to look at the broader scope of affirmative action policies on a case by case basis. One the one hand sometimes it’s just random chance. In theory any given workplace should represent the local demographics, but if you get too granular(workplace, department, specific job title, etc.) then it can become impossible to represent everybody, or just one or two employees can skew the whole demographic.
Where they are a good idea is when you have a group that’s been marginalized over generations that often leads to standards with inherent bias. We could say that college admissions based solely on grades are fair, but that assumes that the process of achieving those grades was fair. In reality, you get situations where people of generational wealth have better access to educational resources, and people from communities that aren’t marginalized find it easier to get higher paying jobs which lets them live in higher income areas with more well funded schools.
That said, there are some inherent biases that are more difficult to overcome. Women are generally less physically capable than men, native English speakers will always have an advantage in some fields over people who have English as a second language. There’s a fine line between unreasonably lowering standards to support minority groups, and taking steps to overcome unfair biases present in those standards.
Questionable. In Northern Ireland, the police used to be something like 7% Catholic, policing a population that was over 40% Catholic. It was controversial at the time, but a 50:50 recruitment policy was put in place in the mid to late 90s, until the balance of the police force matched the wider community.
This is now very broadly accepted as a necessary and beneficial move. The current police force is generally seen as impartial (in terms of this one issue) while the old one was generally not trusted by the minority, to put it mildly.
I think it’s important to look at the broader scope of affirmative action policies on a case by case basis. One the one hand sometimes it’s just random chance. In theory any given workplace should represent the local demographics, but if you get too granular(workplace, department, specific job title, etc.) then it can become impossible to represent everybody, or just one or two employees can skew the whole demographic.
Where they are a good idea is when you have a group that’s been marginalized over generations that often leads to standards with inherent bias. We could say that college admissions based solely on grades are fair, but that assumes that the process of achieving those grades was fair. In reality, you get situations where people of generational wealth have better access to educational resources, and people from communities that aren’t marginalized find it easier to get higher paying jobs which lets them live in higher income areas with more well funded schools.
That said, there are some inherent biases that are more difficult to overcome. Women are generally less physically capable than men, native English speakers will always have an advantage in some fields over people who have English as a second language. There’s a fine line between unreasonably lowering standards to support minority groups, and taking steps to overcome unfair biases present in those standards.