MIL-OSI USA: Representatives Ramirez, Bonamici, Levin Introduce Legislation to Cap Student Loan Interest Rates...

  • Bias Rating

    -26% Somewhat Liberal

  • Reliability

    40% ReliableFair

  • Policy Leaning

    -4% Center

  • Politician Portrayal

    46% Positive

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.

Sentiments

Overall Sentiment

48% Positive

  •   Liberal
  •   Conservative
SentenceSentimentBias
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Bias Meter

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Bias Meter

Contributing sentiments towards policy:

53% : Student loan debt falls particularly hard on servicemembers, given the hurdles they must navigate to receive the benefits they are entitled to.
52% : According to a report by the U.S. Government Accountability Office, as of 2020, nearly 200,000 active-duty servicemembers held federal student loan debt, and nearly half of those balances were more than $13,000.
51% : ""We have an obligation to support the Americans who serve our country, and that includes preventing servicemembers from being saddled with unmanageable student loan debt.
49% : Our bill would help servicemembers tackle student debt by extending interest rate limitations to student loans incurred before entering the military, and therefore cap how fluctuating rates may impact servicemembers' pocketbooks," said Congressman Levin.
46% : Currently, the SCRA protects servicemembers from high interest rates on school loans by capping them at 6%, which applies to student debt that servicemembers or their spouses incurred before entering the military.
44% : With an outstanding balance of more than $1.6 trillion, student loan debt has surpassed credit cards as the largest cumulative consumer debt in America, second only to mortgages.

*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.

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