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Record W4417194917 · doi:10.1177/27538796251395446

Not a voluntary, sustainable, or scalable outcome: Resilience in urban settings of fragility in sub-Saharan Africa

2025· article· en· W4417194917 on OpenAlexaff
Katja Starc Card, Blaise Muhire, Joseph Wilson, Cecilia Ballah Akawu, B.A. Bukar, Sandra Rincon, Andrew Meaux, Zara Ahmadu, Melissa Pavlik, Belen Fodde, Haruna Kuje Ayuba, Connor Smith

Bibliographic record

VenueEnvironment and Security · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsInstitute on Governance
FundersUnited States Institute of Peace
KeywordsFragilityResilience (materials science)UrbanizationPrecarityPsychological resilienceCorporate governanceGovernment (linguistics)Psychological interventionUrban resilience

Abstract

fetched live from OpenAlex

This article focuses on urbanization in settings of fragility, addressing the research question of how new and older settlers manage natural resource use and competition in increasingly urbanizing settings of fragility. Based on two case studies of intermediary sub-Saharan African cities, we find that new and older settlers demonstrate everyday resilience in managing their shared compounded, persistent precarity. This precarity involves constant difficulty in securing water amidst three larger contextual forces: urban poverty, absence of inclusive governance and transparent and fair distribution, and a dynamic landscape of interventions by multiple actors to cope with this precarity. We further find that this resilience was somewhat efficacious in mitigating against the rise of major violence between new and older settlers, though this efficacy may well be highly contingent. We draw on these findings in arguing that resilience may not be a voluntary, sustainable, or scalable solution to increasing urban fragility. Alternative conceptual and practical approaches are finally outlined.

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 score0.121
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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