Bibliographic record
Abstract
When the 2008 crisis hit, all four countries (Canada, Denmark, Ireland and Spain) were in relatively good shape fiscally. Within a few short years the public sector and its finances were not only implicated in, but also targeted as causing, a crisis of profligate spending. While the bulk of this book focuses on cutbacks, retrenchment and restructuring, in this chapter we expose the significant commitments to capital made in the name of austerity. Thus, the austerity era should not be confused with one where public sector spending is simply curtailed; instead state support for capital is often extended in new and familiar ways. In short, there is a lot of spending to account for in times of austerity. In this chapter we: 1) provide an economic background for, and brief overview of, fiscal adjustments and drivers of spending in an austere time – these being related most closely to a) domestic economic imbalances, b) financial and housing bubbles, and c) exposure to international economic downturn; and 2) summarize the massive bank bailouts and aid to the financial sector that each country offered capital in the wake of the 2008 crisis (often institutionalized through public sector agencies). We begin with national snapshots of each country's fiscal–financial condition when the 2008 crisis first hit, weaving in, where appropriate, an historical overview. Next, we provide a thematic description of the nationspecific bailouts and banking sector guarantees (insulations): a) stimulus and guarantees (credit underwriting, insurance support and the like), b) bailouts (asset taking, nationalization), c) write-offs and taxpayer-borne risks, d) partnerships and privatization, and e) international pressures (from supranational institutions and investors). We also note monetary policy exhaustion through rock-bottom interest rates (see Figures 3 and 4 in Chapter 1), exchange rate concerns (Canada, Denmark), and Eurozone monetary stability initiatives (Ireland, Spain). These themes are interrelated and overlap with national peculiarities and identifiably cross-cutting varieties. The chapter concludes with a comparison and analysis of similarities and differences, discussing the range of government and institutional responses and providing explanations for the crisis-driven political economy of debt and finance (shifting the dynamics of insinuation away from banking through institutionalization and insulation).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".