Bibliographic record
Abstract
Introduction This chapter examines the policies and strategies that could comprise an alternative to current austerity policies being pursued in a number of nation states. It outlines the need for a more coherent and robust approach to public investment-led growth to achieve a sustainable recovery that incorporates preventative reforms essential to minimise the occurrence and impact of future financial crises. At the core of this, it is argued, is the need for increased industrial and infrastructure investment, the reform of financial markets, the reconstruction of public services, a redistribution of national income and more stringent controls on the corporate sector. The discussion of these elements of economic management suggests that these policies, rather than austerity, could underpin more effective and more equitable social policies in future. Most G20 governments responded to the financial crisis with fiscal stimulus initiatives. For example, between 2008 and 2010, 15 governments planned to increase infrastructure investment, mainly in transportation networks (IMF, 2009). The expectation was that such investment would increase economic output and maximise job creation with the package of policies estimated to increase gross domestic product (GDP) by 0.4% to 1.2% in 2009 (Whitfield, 2010). For example, the American Recovery and Reinvestment Act 2009 (ARRA) launched a US$782 billion programme with 19% allocated to infrastructure, 35% for unemployment insurance, 24% for tax cuts and 22% for state and local government education and healthcare. However, the programme failed to generate a strong recovery because it relied too heavily on tax cuts as a means of bolstering private spending; household wealth declined dramatically during the recession which in turn weakened the willingness of households to increase spending. In addition to this, credit markets were locked up, especially for smaller businesses (Pollin, 2012). This strategic response to the financial crisis was strongly contested by the German government and European Central Bank, which lobbied instead for fiscal consolidation. By the time of the G20 Toronto meeting in 2010 they had the support of the UK and Canada, leaving the US isolated (Blyth, 2013). The G20 agreed a new ‘fiscal consolidation’ strategy for advanced economies (G20, 2010). The resulting austerity measures ranged from deep public spending cuts, privatisation of state assets, the bailout of failed banks and industrial companies, income and sales tax increases, reductions in and/or more restricted access to welfare benefits, to public sector wage cuts and increased pension contributions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".