Supreme Court case could end affirmative action policies once and for all
View Original Article80% Extremely Conservative
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*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.
14% Somewhat Conservative
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*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.
66% Negative
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Reliability Score Analysis
The Reliability Score of the article is determined on a percentage score basis from 0 to 100%.
- Opposite Sources as Poor for the lower number of sources with different viewpoints.
- Unique Sources as Poor for the lower number of different sources.
- Multiple Sources as Poor for the lower number of total sources.
- Multiple Quotes as Poor for the lower number of quotes used in the article.
- Quote Length as Poor for the lower number of words used in each quote.
Opposite Sources: 0% Poor (Grade F) Unique Sources: 0% Poor (Grade F) Multiple Sources: 0% Poor (Grade F) Multiple Quotes: 0% Poor (Grade F) Quote Length: 0% Poor (Grade F)
Bias Score Analysis
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
This article includes the following sentiments, providing an average bias score of 14% Conservative:
- 1 positive sentiment and 1 negative sentiment for Affirmative Action.
"And since 2016, President Donald Trump appointed three new justices, dramatically altering the makeup of the court and potentially giving support to Justices Samuel Alito, Clarence Thomas, and John Roberts, each of whom forcefully dissented in 2016 and harshly criticized affirmative action policies."
"The Supreme Court on Monday said it will hear a challenge to affirmative action policies in a case that will consider wither the admissions process at certain universities discriminates against students of Asian descent."
Policies:
Affirmative ActionSentiments
- Liberal
- Conservative
-78% "And since 2016, President Donald Trump appointed three new justices, dramatically altering the makeup of the court and potentially giving support to Justices Samuel ..."
*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. The rating is an independent analysis and is not affiliated nor sponsored by the news source or any other organization.
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Contributing sentiments towards policy:
55% : The Supreme Court on Monday said it will hear a challenge to affirmative action policies in a case that will consider wither the admissions process at certain universities discriminates against students of Asian descent.11% : And since 2016, President Donald Trump appointed three new justices, dramatically altering the makeup of the court and potentially giving support to Justices Samuel Alito, Clarence Thomas, and John Roberts, each of whom forcefully dissented in 2016 and harshly criticized affirmative action policies.
*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.