Bibliographic record
Abstract
Chapter 5 demonstrated that public responsiveness to policy is pervasive. This is satisfying and important, and it provides a corresponding basis for policy representation. Before considering representation, however, this chapter probes further public responsiveness to budgetary policy, focusing on “to what” and “when” the public responds. It does so by addressing five issues related to public responsiveness, each an extension of the basic thermostatic hypothesis investigated in Chapter 5. The issues are in one sense rather disparate, and take the analysis of public responsiveness in different directions. But the results all do bolster the claim that what we have identified as public responsiveness to budgetary policy is actually that. First, we examine the degree to which the public responds to policy decisions versus policy outputs, that is, to the making of budgetary policy versus to expenditures as they occur. Second, we explore when in the fiscal year the public responds; this also helps us understand to what the public responds, as we will see. Third, we consider whether the public is responding to the outcomes of spending policy rather than to spending itself. We examine, for example, the degree to which public responsiveness is focused on changes in crime spending or on changes in the crime rate. Fourth, we also explore the degree to which responsiveness in a federal context is focused on spending by a single government, versus the spending of multiple governments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".