Resisting austerity measures to social policies: multiple explanatory case studies
Bibliographic record
Abstract
Since Margaret Thatcher reached power in the United Kingdom, European governments have increasingly turned to neoliberal forms of policy-making, focusing, especially after the 2008 Great Recession on 'austerity policies' rather than investing in social protection policies. We applied a multiple explanatory case studies methodology to examine how and why challenges and resistance to these austerity measures are successful or not in four settings for three different social policy issues: using a gender lens in state budgeting in Andalusia (Spain), maintaining unemployment benefits in Italy and cuts to fuel poverty reduction programs in Northern Ireland and England. In particular, we intended to learn about whether resistance strategies are shared across disparate cases or whether there are unique activities that lead to successful resistance to austerity policies. As our approach drew from realist philosophy of science, we started with initial theories concerning collective action, political ideology and political power of affected populations. Our findings suggest that there are similarities between the cases we studied despite differences in political and policy contexts. We found that joint action between advocacy groups was effective in resisting cuts to social spending. Evidence also indicates that the social construction of target populations is important in resisting changes to social programmes. This was observed in both England and Northern Ireland where pensioners held significant political clout.
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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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".