The Tortoise or the Hare? Incrementalism, Punctuations, and Their Consequences
Bibliographic record
Abstract
In this article, we contrast the long‐term consequences of incrementalism and punctuated equilibrium. We test what the impact of each of these types of policy change is on long‐term budgetary outcomes for the American states. Policy scholars have applied both theoretical approaches to the study of budgetary spending as an extension of policymaking. Given the two contrasting paradigms of policy change, we develop the following line of inquiry: Does punctuated equilibrium create a different budget in the long term than incrementalism? We address this question through an analysis of American state budgets because the U.S. states provide a rich variation in both budgetary outcomes and political institutions. We use budget data from all American states across all government functions for the period between 1984 and 2009. We find that, first, state budgets and budget functions vary in their degree of punctuation and, second, the degree of punctuation in a state's budget function corresponds to smaller long‐term growth. Additionally, the kind of spending matters: allocational budget categories are more likely to exhibit punctuations.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".