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Record W4404762614 · doi:10.1080/02723638.2024.2428076

Cities for a guaranteed income: renewing the urban politics of cash assistance in the United States

2024· article· en· W4404762614 on OpenAlexaff
Jamie Peck, Nik Theodore

Bibliographic record

VenueUrban Geography · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPoliticsCashLow incomeEconomic growthBusinessPolitical scienceDevelopment economicsEconomicsFinanceSocioeconomicsLaw

Abstract

fetched live from OpenAlex

This paper explores the punctuated and uneven historical geography of the city-led “guaranteed income” movement in the United States, which since its recent (re)birth in Stockton, California and Jackson, Mississippi has spread to more than 100 cities. What can be described as a multi-city movement for basic-income provision has spawned a thriving ecosystem comprising competing experiments, active policy networks, philanthropic funding circuits, replicable program designs, organic intellectuals, advocacy-cum-evaluation centers, and communities of practice. Anchored at the urban scale but multipolar in form, this represents a distinctively American approach to basic-income programming, animated mostly from below by way of municipal models, local mobilization, and civic leadership. (Re)born as a municipal policy model, guaranteed income has taken life as a movement, which some have likened to a “quiet revolution.” Problematizing the spaces and scales of basic-income policymaking in the United States, the paper constructs a middle-range, purposefully historicizing pathway, beginning during the civil rights era and punctuated by the reactionary turn to welfare retrenchment and neoliberal workfare. As such, it moves between the urban, interurban, and extraurban scales in pursuit of a conjuncturally situated explanation of recent policy developments.

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)0.001
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 score0.884
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.021
GPT teacher head0.221
Teacher spread0.201 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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