Bibliographic record
Abstract
Theodore (2020: 2) argues that since the global financial crisis, “austerity has become the primary means for the further neoliberalisation of inherited arrangements”: neoliberalisation upon earlier waves of neoliberalism. Chapter 2 delves into this proposition. It begins by exploring the impact of the GFC, and its aftermath, in the eight countries and cities studied. It proceeds to examine the interplay of key terms introduced in chapter 1: crisis, austerity and neoliberalisation. The chapter allocates the cities to three groups: those in which austerity is recognized as a central concept or challenge and a warrant for neoliberalisation (Athens, Dublin and Leicester), those in which it is concealed or re-signified within an otherwise vigorous neoliberalisation agenda (Baltimore and Montréal), and those positioning themselves critically or outside it (Barcelona, Dandenong and Nantes). The chapter concludes by discussing theoretical implications of convergence and divergence in the cross-cutting relationships between crisis, austerity and neoliberalisation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".