Bibliographic record
Abstract
Public budgets might be thought of as technocratic documents that set out planned revenues and expenditures for the fiscal year. But with fiscal policy connected to all aspects of government activity, budgets hold significant repercussions for a wide range of actors including the state, private enterprise and labour. Budgets focus the relationship between government and economy: where and for whom spending and revenue are directed or diverted, implicating existing institutions, empowering (new or established) agencies, insulating or exposing particular segments of society, and insinuating degrees of blame and responsibility for both the good and bad times. Budgetary analyses must therefore consider the political economy context of fiscal crises, revenue and spending, and the relationship between state expenditures and private enterprise. The (re)allocation of public money by the state reflects the political priorities and choices of government. The normative dimension of budget allocation is a prime expression of its politics. Consequently, the pre-eminent position of ministries of finance within the state ought to be no surprise. Likewise, monetary policy, commonly the preserve of independent central banks, affects not only the money supply but also interest and exchange rates, conditioning the behaviour of many economic sectors and government departments. Other strictures like balanced budget legislation and cost control equally pressure public decision makers, often with more to say on expenditures than revenues (especially revenue beyond taxation). In this chapter on selling restraint through the politics of public sector budgeting, we analyze the fiscal narratives of Canada, Denmark, Ireland and Spain through a political economy lens that understands fiscal policy, public sector spending, cuts and reallocations to be intrinsically political activities, which fundamentally delineate the contours of the state–society relationship through changes (or lack thereof) on both revenue and expenditure sides of the ledger.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".