The End of Affirmative Action
- Bias Rating
-18% Somewhat Liberal
- Reliability
50% ReliableFair
- Policy Leaning
-16% Somewhat Liberal
- Politician Portrayal
-63% Negative
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The A.I. bias rating includes policy and politician portrayal leanings based on the author’s tone found in the article using machine learning. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral.
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Policy Leaning Analysis
Politician Portrayal Analysis
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Contributing sentiments towards policy:
63% : And two, Sotomayor and Clarence Thomas, have said that they benefited from affirmative action.59% : Few colleges outside of elite institutions have affirmative action policies; they accept a majority of applications.
55% : Even in liberal California, the public has voted twice to prohibit affirmative action.
55% : The public's views could make it difficult for Democrats to rally Americans in support of affirmative action as they have with abortion rights since the court overturned Roe v. Wade last year.
55% : But one large university system, the University of California, adopted policies that helped increase the number of Black and Hispanic students after the state ended affirmative action.
50% :Whether a justice views affirmative action as positive or negative appears to hinge on whether he or she primarily sees it as holding down or pulling up prospective students.
50% : The most passionate opposition to affirmative action centered on its most reparative element: compensation for centuries of exclusion, Jerome Karabel writes for Times Opinion.
46% : The current Supreme Court has been out of step with public opinion in some of its highest-profile rulings, including on abortion and environmental protection.
40% :Affirmative action created a race-obsessed American university culture, and the court ruling will make it worse, Tyler Austin Harper writes for Times Opinion.
35% : He stated that affirmative action is racially discriminatory and unconstitutional.
*Our bias meter rating uses data science including sentiment analysis, machine learning and our proprietary algorithm for determining biases in news articles. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral. The rating is an independent analysis and is not affiliated nor sponsored by the news source or any other organization.