When the party's over the politics of fiscal squeeze in perspective
Bibliographic record
Abstract
The politics of cutting public spending or raising taxes (or both) has dominated politics in many democracies in recent years. A new era of conflict has developed, with old political alignments being tested and new battles emerging over whose expectations are to be disappointed and who should be blamed for fiscal squeeze. Do parties who cut spending always go down to defeat in elections? Are there 'best practice' cases that every government should follow when it has to cut spending or raise taxes to balance its public finances? Such issues have mainly been analysed from an economic or financial perspective and in the context of recent cases. By contrast, this book focuses on the politics of fiscal squeeze and takes a longer view. It combines quantitative and qualitative analysis to examine cases ranging from the fiscal squeeze in the United States in the 1830s/40s (when half of the states then in the Union defaulted) to the squeeze following the 2001 Argentinian default. It assesses who were the winners and losers, who got the blame and what were the longer-term effects on politics and government. It argues that 'how to do it' approaches to fiscal squeeze in democracies, based on apparently successful cases, often fail to take into account profound differences in circumstances. Available in OSO: Contributors to this volume - Sebastian Dellepiane-Avellaneda is a Lecturer in the School of Government and Public Policy at the University of Strathclyde, Glasgow. Chris Eichbaum is Reader/Acting Head of School, School of Government at Victoria University of Wellington. Robert (Bob) Gregory is an Adjunct Professor of Politics in the School of Government, Victoria University of Wellington. Niamh Hardiman is Senior Lecturer in the School of Politics and International Relations, University College Dublin. David Heald is Professor of Accountancy at the University of Aberdeen Business School. Rozana Himaz is a lecturer in Economics, Queen's College Oxford. Christopher Hood CBE FBA is Gladstone Professor of Government, All Souls College, Oxford. Walter Kickert is Professor of Public Management at Erasmus University Rotterdam and also scientific director at the Netherlands Institute of Government. Anders Lindbom is a Professor in Political Science at Uppsala University, Sweden. Martin Lodge is Professor of Political Science and Public Policy at the Department of Government and the Centre for Analysis of Risk and Regulation at the London School of Economics. Alasdair Roberts is the Jerome L. Rappaport Professor of Law and Public Policy at Suffolk University Law School, Boston USA. Donald J. Savoie holds the Canada Research Chair in Public Administration and Governance at the Universite de Moncton. Kai Wegrich teaches Public Administraton and Public Policy at the Hertie School of Governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".