US to end race-based university admissions: what now for diversity in science?
- Bias Rating
-10% Center
- Reliability
80% ReliableGood
- Policy Leaning
-10% Center
- Politician Portrayal
22% 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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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
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Contributing sentiments towards policy:
59% : "So, affirmative action is just another data point in that line of a historical trend."54% : "Affirmative action was the only tool that helped us bring in students that weren't from wealthy families or wealthy institutions," Flores says.
50% : The reason that some US universities have adopted race-conscious admissions -- sometimes referred to as affirmative action -- is to address centuries of racism and exclusion in the United States.
41% : "The attacks on affirmative action are really attacks against efforts to remedy past harm against Black people," Welbeck says.
*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.