The big idea: why we shouldn't be levelling up
- Bias Rating
-74% Very Liberal
- Reliability
N/AN/A
- Policy Leaning
74% Very Conservative
- Politician Portrayal
-27% 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.
Sentiments
N/A
- Liberal
- Conservative
Sentence | Sentiment | Bias |
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
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-100%
Liberal
100%
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Contributing sentiments towards policy:
56% : The thinking was that these acquisitions, the fruits of the war over Brexit, could not be kept once Brexit was "done" unless their needs were addressed.48% : The gap between real funding per head in state and private schools is widening.
47% : Providing decent education, health and social care and green energy is not - and we should focus on those things instead.
45% : Devolving tax and spending limits the possibility of redistribution from richer areas to poorer ones; it unravels the fiscal union, setting the scene for the kinds of difficulties the euro area experienced after the financial crash.
45% : Government has to deal with the crisis in social care.
45% : This is a long list of policies that are expensive but essential, and will stretch government capacity and the electorate's tolerance of taxation to its limits.
43% : Internal opposition aside, the pressure to keep taxes as low as possible, and the other calls on the government purse, greatly limit the cash available to make levelling up a reality.
38% : It was a way to consolidate the coalition brought together by Brexit so that it would have a life beyond Brexit itself.
36% : For instance, better funding for the NHS and social care will help close one of the worst aspects of inequality, the gap in life expectancy between rich and poor.
*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.