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Record W3154488560 · doi:10.1525/sod.2019.0017

Seeing the Local State

2021· article· en· W3154488560 on OpenAlexaff
Linda Lobao, Alexandra Tsvetkova, Gregory Hooks, Mark D. Partridge

Bibliographic record

VenueSociology of Development · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPovertyProsperityLocal governmentInequalityRecessionState (computer science)Development economicsNeoliberalism (international relations)Political scienceCulture of povertyLocalismEconomic growthEconomicsPolitical economyBasic needsPublic administrationPolitics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.222
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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