Stability and Punctuations in Public Spending: A Comparative Study of Budget Functions
Bibliographic record
Abstract
This article provides a comprehensive analysis of stability and punctuations in public spending within and across two different countries—Denmark and the United States. The theoretical starting point is the classic model of budget incrementalism and Jones and Baumgartner's model of disproportionate information processing. First, despite the clear differences in institutional setup, we show that public spending spanning many decades in Denmark and the United States are characterized by a similar distribution of small-, medium-, and large-scale spending changes. What is more intriguing is that we show how this aggregate result obscures (1) substantial variation between categories of public spending and (2) similar tendencies within similar spending categories across the two countries. These findings suggest that we need to unpack the overall budgets for detecting the particular sources of stability and change in government spending. Hence, the article offers important comparative findings that not only challenge the empirical validity of classic budgetary incrementalism but also advocate an increased focus on more disaggregated spending dynamics than employed in previous studies of the model of disproportionate information processing.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".