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Record W4281747530 · doi:10.1080/19376812.2022.2077781

Trapped or not trapped? An empirical investigation into the lived experiences of the urban poor in Harare’s selected informal settlements

2022· article· en· W4281747530 on OpenAlexaff
Elmond Bandauko, Senanu Kwasi Kutor, Robert Nutifafa Arku

Bibliographic record

VenueAfrican Geographical Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsUniversity of TorontoWestern University
FundersRoyal Geographical Society
KeywordsInformal settlementsHuman settlementLivelihoodPovertyNexus (standard)SlumAmbivalenceSettlement (finance)GeographyEconomic growthEmpirical researchSociologySocioeconomicsPolitical sciencePsychologyBusinessPopulationSocial psychologyArchaeology

Abstract

fetched live from OpenAlex

The role of informal settlements in human development remains contested in urban studies literature. For instance, some existing studies view urban informal settlements as hotspots of social unrest, squalor and precarious residential environments (poverty traps); while others perceive them as places where the poor become resourceful, ingenious, and develop necessary skills to navigate urban life (pathways out of poverty). The absence of systematic evidence on the nexus between informal settlements and human progress hinder sound urban policy practices. This paper examines the role of informal settlements in human development focusing on Hopley, Hatcliffe Extension and Epworth Ward 7–Harare’s three largest informal settlements. The study combines surveys, in-depth interviews, and focus group discussions with selected residents from the three neighborhoods. The study reveals that despite feeling ‘trapped’ in conditions of precarious, overcrowded, and insecure housing, coupled with discursive territorial stigmatization, some informal settlement residents are hopeful that their settlements will eventually improve. The ambivalence of Harare’s urban policy toward informal settlements must be replaced by a more positive approach to improve the livelihoods of people living in these neighborhoods.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0050.007
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.319
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations25
Published2022
Admission routes1
Has abstractyes

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Same venueAfrican Geographical ReviewSame topicUrban and Rural Development ChallengesFrench-language works237,207