Is austerity coming? The new politics of public debt after Covid-19
Bibliographic record
Abstract
The old politics of public debt is austerity - aggressive spending cuts to repay debt. Intuitively, the post-pandemic environment is an opportunity for progressive political-economic change, although the same was true after the 2008 global financial crisis. After 2008, many countries saw the increase of austerity ideas, with governments restricting spending to avoid public debt. Blyth argues 'we were all temporary Keynesians' during the 2008 recession, but the 'dangerous idea' of austerity returned. Graeber also argues debt creates 'moral confusion', which causes voters to accept the logic of repaying debt and consequential spending cuts. With significant increases in debt occurring during the pandemic, we can ask whether we are again only temporarily Keynesians, whether austerity politics will return, or whether a new politics of public debt is emerging that aims to avoid class-based resistance to spending cuts but grow the economy so that debt is less of a problem. While research cannot predict future developments, it can explore the factors that have influenced previous austerity policies. This thesis conducts a content analysis of government and opposition speeches on annual government budgets in the United States, United Kingdom, Canada and Australia to test how rhetoric has evolved since the late 1980s in response to increased public debt caused by the early 1990s, 2008 and 2020 recessions. It argues political parties have gradually reframed the urgency of repaying debt and softened spending restriction rhetoric since 1990. The analysis demonstrates a long-existing trend of parties reframing rhetoric about the causes and proposed solutions to increased debt. With the benefit of a longer-term view of the 2008 crisis, evidence indicates it was austerity that was temporary, rather than Keynesianism. The thesis builds upon previous work that has highlighted factors that encourage parties to repay debt, including the power of capital interests and moral anti-debt or austerity ideas. It does so by aligning existing theory with other recession-crisis problem-solving theories, such as those of Streeck and Hall. Together, these perspectives help to develop theoretical explanations for the tendency of governments to expand debt. [...]
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".