Reeves rejects claims she misled public over Budget 'black hole'
- Bias Rating
- Reliability
40% ReliableAverage
- Policy Leaning
50% Medium Right
- Politician Portrayal
N/A
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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
-24% Negative
- Liberal
- Conservative
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Reliability Score Analysis
Policy Leaning Analysis
Politician Portrayal Analysis
Bias Meter
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Conservative
Contributing sentiments towards policy:
53% : While the watchdog did cut productivity forecasts, costing £16 billion in expected tax receipts, much of this was offset by higher inflation and wage growth, leaving Ms Reeves with a £4.2 billion surplus under her borrowing rules.51% : " Ms Reeves also noted that without the productivity hit she would have had £20 billion of headroom, before factoring in welfare spending.
49% : "The only thing that was unfunded was the welfare payments which she has made and she's doing it on the backs of a lot of people out there who are working very hard and getting poorer.
47% : She had fuelled some of that speculation herself on 4 November, warning in a Downing Street speech that weaker productivity would mean "lower tax receipts" and "consequences for the public finances".
46% : She added: "In the context of a downgrade in our productivity, which cost £16 billion, I needed to increase taxes, and I was honest and frank about that in the speech that I gave at beginning of November.
40% : On Sunday she stressed that this would have been the smallest headroom any chancellor had secured, and it did not account for later decisions such as reversing winter fuel payment cuts, welfare reform, or scrapping the two-child benefit cap -- a policy expected to lift 450,000 children out of poverty.
37% : "She was raising taxes to pay for welfare.
*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.