Understanding Austerity: Its Reach and Presence in the Changing Context of Work and Employment
Bibliographic record
Abstract
Let me put it very forcefully: No large economy has ever recovered from an economic downturn through austerity. It's not going to happen in the United States, and it's not going to happen in Europe. (Joseph Stiglitz) Introduction This introductory chapter sketches out some of the major debates concerning austerity, neoliberalism and work. Given the international content in this volume, austerity debates are sketched in broad strokes rather than being specific to national contexts. Austerity is viewed as a set of interwoven policies aimed at reducing public debt and expenditure, increasing consumer taxes and purportedly stimulating economic wellbeing through corporate tax cuts and support for private business. Since the 1970s, austerity policies have been closely associated with neoliberalism, a set of policies and processes that valorize the private market as the solution to all social and economic problems and seek to reduce or eliminate social entitlements and public provision (Harvey, 2007; Evans and McBride, 2017). Austerity policies are underscored by neoclassical economics assertions that cutting public budgets and reducing government debt will generate ‘confidence’ and a return to growth and prosperity during difficult economic times (Blyth, 2013, p 3). The current wave of austerity policies stems from the aforementioned neoliberal economic consensus that pervades developed economies, albeit in an uneven way. The current iteration of austerity, rather than its ongoing presence as an integral part of neoliberalism (Pierson, 1998; Clarke, 2017), was triggered by the global financial crisis (GFC) of 2007– 08. Neoliberalism's emphasis on creating freedom of capital and lifting restrictions on international finance (Harvey, 2007) led to the financial ‘light touch’ regulation that governed the US sub-prime and other national housing markets. The financial crisis that followed the collapse of these housing markets threatened the entire global economy (Lethbridge, 2012). The initial government responses in 2008–09 were fiscal stimulus packages, which increased government borrowing and levels of debt, and simultaneously prevented deep and lasting recessions. The second stage of crisis involved governments taking the opportunity of crisis to reduce the real and purportedly high accumulated levels of debt created by the GFC and previous decades of social spending (Mendoza, 2014; Stiglitz, 2014; Evans and McBride, 2017).
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.041 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".