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Record W4224988089 · doi:10.1111/anti.12837

The<scp>Post‐Political</scp>Violence of Racial Property Regimes: Maintaining Gardens’ Land Insecurity through Abstract Codes in East Harlem,<scp>NYC</scp>

2022· article· en· W4224988089 on OpenAlexfundno aff
Chantal Gailloux

Bibliographic record

VenueAntipode · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGentrificationExpropriationInjusticeCommodificationPublic spacePoliticsSociologyPolitical sciencePolitical economyPublic administrationEconomyLawEconomic growthEconomics

Abstract

fetched live from OpenAlex

Abstract Six community gardens on City‐owned land in East Harlem have been evicted and relocated for a private mixed‐income and mixed‐use development under Mayor de Blasio’s citywide affordable housing plan, Housing New York. Although the licence agreements enabling gardeners to use public land contained a clerical error concerning which lots gardeners could use, public–private coalitions generating urban space production forcefully affirmed their authority on the interpretation of documents, feeding into the bureaucratic violence of spatial abstraction and racial land injustice. Licence agreements are devices of control over space and its users, and tools of abstraction toward accumulation and racial capitalism. Black and Brown community gardeners have led efforts of urban regeneration when the City rolled back public services in inner‐city neighbourhoods and appropriated land improvements through licence agreements. Housing New York is now catalysing urban regeneration—or City‐led gentrification—for middle‐income and wealthy earners in neighbourhoods with a past of decline. Through the violent story of spatial abstraction and commodification of land used as community gardens, I show how the contestation around a piece of land during participatory governance unfolds through the state’s demonstration of authority with an aura of legality and transparency.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.223
Teacher spread0.205 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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