Stuck in neutral? Federalism, policy instruments, and counter-cyclical responses to COVID-19 in the United States
Bibliographic record
Abstract
Federalism plays a foundational role in structuring public expectations about how the United States will respond to the COVID-19 pandemic, as both an unprecedented public-health crisis and an economic recession. As in prior crises, state governments are expected to be primary sites of governing authority, especially when it comes to immediate public-health needs, while it is assumed that the federal government will supply critical counter-cyclical measures to stabilize the economy and make up for major revenue shortfalls in the states. Yet there are reasons to believe that these expectations will not be fulfilled, especially when it comes to the critical juncture of the COVID-19 pandemic. Though the federal government has the capacity to engage in counter-cyclical spending to stabilize the economy, existing policy instruments vary in the extent to which they leverage that capacity. This leverage, we argue, depends on how decentralized policy arrangements affect the implementation of both discretionary emergency policies as well as automatic stabilization programs such as Unemployment Insurance, Medicaid, and the Supplemental Nutrition Assistance Program. Evidence on the US response to COVID-19 to date suggests the need for major revisions in the architecture of intergovernmental fiscal policy.
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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 | 0.006 | 0.011 |
| 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 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".