Seeing the Local State
Bibliographic record
Abstract
Sociologists have long recognized uneven development within nations and differential patterns of poverty and prosperity across places. In analyzing why some places fare better than others, researchers largely focus on market forces. Few studies have considered the role of the local state. Yet in many countries today local governments have gained responsibilities and control as national governments offload responsibilities. This shift toward localized government is often associated with neoliberalism. The conventional view is pessimistic about local governments, stressing their potential to reinforce poverty and inequality. Our research challenges this view. We advance a counter-perspective that builds from two subnational literatures, one on poverty and place and the other, mesocomparative research on the state. Focusing on the United States, we examine whether local governments are linked to poverty and income inequality. Using unique data that span all communities (over 3,000 county areas) over the Great Recession, we show that the institutional capacity and spending policy of local governments at the outset of the recession influenced how communities fared subsequently. To our knowledge, this is the first sociological study that integrates theoretical understanding of local state processes and research aimed at explaining poverty and inequality across the United States.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".