New Jersey Orders School Districts to Cease Identification Process of Migrant Children, Inadvertantly Green Lighting School Choice
- Bias Rating
-8% Center
- Reliability
25% ReliablePoor
- Policy Leaning
-8% Center
- Politician Portrayal
N/A
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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.
Sentiments
-22% Negative
- Liberal
- Conservative
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
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-100%
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100%
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Contributing sentiments towards policy:
60% : The department is committed to ensuring that schools across the state comply with both state and federal laws, which mandate that public education must be available to all children.57% : Ensuring Equal Access to EducationThe NJDOE's memo underscores the importance of ensuring that all children, regardless of immigration status, can access public education without fear or unnecessary hurdles.
55% : Federal and State ProtectionsThe NJDOE memo follows years of legal precedent aimed at ensuring that all children, regardless of their immigration status, have access to public education.
53% : Under New Jersey Administrative Code (N.J.A.C. 6A:22-3.3), public schools must admit all students between the ages of 5 and 20 who are domiciled in the district or otherwise entitled to attend, regardless of their immigration or visa status.
42% : The Plyler v. Doe decision established that public schools cannot exclude students from attending based on their immigration status.
41% : This move is intended to ensure that no child is denied access to public education based on their or their family's immigration status.
36% : In Plyler v. Doe, the U.S. Supreme Court ruled that denying public education to undocumented children violates the Equal Protection Clause of the 14th Amendment.
*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.