What does 'community policing' even mean? Outdated buzzwords can't fix policing problem.
- Bias Rating
-12% Somewhat Liberal
- Reliability
N/AN/A
- Policy Leaning
34% Somewhat Conservative
- Politician Portrayal
-11% Negative
Continue For Free
Create your free account to see the in-depth bias analytics and more.
Continue
Continue
By creating an account, you agree to our Terms and Privacy Policy, and subscribe to email updates. Already a member: Log inBias 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
N/A
- Conservative
Sentence | Sentiment | Bias |
---|---|---|
Unlock this feature by upgrading to the Pro plan. |
Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
Extremely
Liberal
Very
Liberal
Moderately
Liberal
Somewhat Liberal
Center
Somewhat Conservative
Moderately
Conservative
Very
Conservative
Extremely
Conservative
-100%
Liberal
100%
Conservative
Contributing sentiments towards policy:
59% : He is so confident in this strategy that his budget proposal includes $30 billion in mandatory spending to support law enforcement, including funding for more community police officers.55% : As the Proactive Policing report explained, law enforcement need the public's support to control crime, and authorities can earn that support by changing the way they engage the public from warrior to a service or guardian approach.
53% : Research shows that when these expectations are met, the public perceives police as legitimate and are more inclined to defer to law enforcement authority in the present and collaborate with police in the future, even to the extent of being more inclined to obey the law.
*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.