否则怎么能根据一纸申诉，就言之凿凿地肯定伊敏的事件是种族歧视呢？即时你是法官，是不是在判决之前至少应该听听被告方的说法呢，同样一件事，被告方和告方从各自的角度描述出完全不同的版本还少吗？罗生门的故事，大家应该也听说过了吧？如果你happen是那位被控告老师的朋友，你是不是因为立场的关系又会换一种说法了呢？
下面普及一下 how prove racial discrimination case（美国人没那么傻，一切得按程序来）， 看过后你再告诉我伊敏这件事如果真的控告种族歧视的话胜算有多少。
1
Decide exactly who is doing the discriminating, and what they are discriminating on the basis of. Is it an employer, a landlord, an industry, a society? Are they discriminating on the basis of race, religion, gender, class, or something else?
2
How is the discrimination manifesting? Are applicants being rejected outright, or are they being offered jobs with lower wages and fewer chances of promotion.
3
Based on the above, decide how you plan to proceed. The most important differences between the two methods described are how many people are doing the discriminating, and how easy it will be to collect the necessary data. The statistical method works best when many people each do a little bit of discrimination, but to be reliable it requires collecting a lot of data. The sting method requires little numerical data, but focuses the attention on a few specific individuals and might miss sporadic discrimination.
Statistical
1
Create a specific hypothesis describing as specifically as possible the nature of the discrimination. A good hypothesis might look like, "Hospitals in California offer lower wages to Hispanics than to equally qualified non-Hispanics."
2
Collect data that could provide evidence for or against such a claim of discrimination. You could look for such data from sources like the United States Census or the Bureau of Labor Statistics or you could conduct an independent survey to collect your own. For the example, you would need information on the wages of a random sample of Hispanic and non-Hispanic California hospital employees. Your data should include any other factors that might affect wages such as education and experience. Include other information about the employees that might affect wages even if you think it shouldn't. You don't want to accuse someone of discrimating against Hispanics when in reality they discriminate against jews. The fact that an ethical company would do neither is no excuse for academic sloppiness.
3
Import all your data into your favorite stastical analysis package. Run a multivariate least squares regression for wages against all the other variables collected.
4
For each variable, look at the coefficient and the p-value. The coefficient represents the magnitude of the discrimination and the p-value represents the likelihood of any discrimination at all. For example if the analysis of your data said that Hispanic had a coefficient of -.54 and a p-value of .02, it would mean that being Hispanic cost an employee $0.54/hr. and there was a 2% chance that the discrepency could be explained by random chance in the absense of racial discrimination (and therefore a 98% chance that it was due to being Hispanic.)
5
Publish your findings. Expect the accused to look for possible shortcomings in your methodology. Don't make their job easier by omitting obviously revelant variables or choosing a biased sample.
Sting Operation
1
Create a specific hypothesis describing the nature of the discrimination. For example, "The landlord of Evergreen Apartments doesn't like renting to African-Americans."
2
Send the individual that you suspect of racial discrimination applications of two people who are equals with respect to financial stability, but of different races. Most applications won't directly ask for race, but it's often possible to guess someone's race from their name. Use a stereotypical white name for one applicant and a stereotypical black name for another.
3
Wait for the responses. If both applicants are treated equally, there is no evidence of racism. If one gets invited to tour the unit and make a deposit while the other is told that there are no vacancies, there is a very good case that the applicant's perceived race was a factor.
4
The case can be strengthened by repeating this test several times. One case where the responses differ is suspicious but might be explained as a mistake, not necessarily racially motivated. Five cases where the "black" applicant fares better than the "white" one is almost indisputable evidence that race was taken into account.
选择“Disable on www.wenxuecity.com”
选择“don't run on pages on this domain”
