Cities for a guaranteed income: renewing the urban politics of cash assistance in the United States
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".