Divide-the-Quarter: Testing Distributive Politics Theories in the States Using Federal Opportunity Zones
Bibliographic record
Abstract
In this short paper we use case in which all 50 governors simultaneously faced similar decisions about allocating a constrained set of valuable resources---tax advantaged status for economic development--- to test political alignment theories of resource allocation alongside two less explored alternatives: spreading the wealth by geography, and policy need. We find that governors prioritized county lines such that sites in counties with fewer opportunities to distribute were disproportionately selected. We also find they were responsive to policy need. However, we do not find evidence of particularism based on the politics of an area's voters or its local elected officials. This work thus provides reason for caution when generalizing from the presidential particularism literature to different institutional contexts and different types of resources. It underscores the primacy of geographic boundaries in political decision making, relative to population and other factors, even when they are not units of vote aggregation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".