It's no wonder I couldn't see a GP: limiting access to services is the point | Kenan Malik
- Bias Rating
-10% Center
- Reliability
N/AN/A
- Policy Leaning
10% Center
- Politician Portrayal
30% Positive
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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
- Conservative
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Policy Leaning Analysis
Politician Portrayal Analysis
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-100%
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Conservative
Contributing sentiments towards policy:
53% : Where the postwar state directly intervened to secure what were seen as desired social and economic outcomes - from creating the National Health Service to the nationalisation of key industries, from railways to coal - the post-Margaret Thatcher regulatory state viewed desired outcomes as best shaped by the market and saw the role of the state less as providing services than in regulating the market and in "steering" it towards desirable outcomes.45% : Over time, the process of regulation itself has been removed from the direct ambit of the government through the creation of independent quangos to provide the "steering", from Ofgem to the Low Pay Commission.
45% : But, more than that, in the age of austerity, mechanisms of regulation have become the means less of ensuring good services than of restricting access to impoverished ones.
44% : The inquiry into the fire has exposed the degree to which private companies not only rode roughshod over government regulations but also the degree to which regulators often colluded with the rule-breakers or at least turned a blind eye to their activities.
40% : This is because the idea that "government should be responsive to the people creates the expectation that government should meet the needs and correct the evils affecting particular groups in society" and makes it impossible "to curtail spending, increase taxes and control prices and wages".
*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.