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Record W4401335592 · doi:10.1080/02723638.2024.2383521

Working toward infrastructural citizenship: state-society relations and community waste labor in Cape Town

2024· article· en· W4401335592 on OpenAlexfundno aff
Kathleen Stokes

Bibliographic record

VenueUrban Geography · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaEconomic and Social Research CouncilDepartment for International Development
KeywordsCitizenshipState (computer science)SociologyPoliticsCapeSpace (punctuation)Conceptual frameworkPolitical scienceEconomic growthSocial scienceEconomicsLaw

Abstract

fetched live from OpenAlex

As a conceptual device, infrastructural citizenship looks to bridge performed and legal interpretations of citizenship, recognizing infrastructure as a space where citizenship is practiced and upon which political identities are based. While emerging discussions surrounding infrastructural citizenship have largely focused on issues of access, this article proposes engaging with infrastructural labor as a way of extending and developing how infrastructural citizenship is understood and leveraged. I begin by bringing infrastructural citizenship into conversation with ongoing geographic discussions surrounding infrastructural labor, and grounding analysis within the particularities of waste geographies and South African community waste schemes. Attending to the labored dimensions of infrastructural citizenship within a state-led community-waste initiative in Cape Town, I outline how community facilitators perceived their labor contributions, and expectations of the state. On this basis, I propose that infrastructural labor merits further consideration within infrastructural citizenship’s emergent framework.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.247
Teacher spread0.232 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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