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Record W4385838989 · doi:10.1080/07352166.2023.2232061

Exploring place attachment dynamics in deprived urban neighborhoods: An empirical study of Nima and Old Fadama slums in Accra, Ghana

2023· article· en· W4385838989 on OpenAlexaff
Elmond Bandauko, Akosua Boahemaa Asare, Godwin Arku

Bibliographic record

VenueJournal of Urban Affairs · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPlace Attachment and Urban Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsSlumPlace attachmentResidencePsychological interventionFeelingGeographyPerceptionSocioeconomicsSociologyPsychologySocial psychologyPopulationDemography

Abstract

fetched live from OpenAlex

Despite the growing body of literature on place attachment, research on this topic in the Global South remains limited. This is especially concerning given the significant impact of residential environments, such as slums, on the lived experiences of their inhabitants. This paper addresses this gap by examining the dynamics of place attachment in Nima and Old Fadama, the two largest slums in Accra. Specifically, the study investigates how residents of these neighborhoods perceive their places of residence and the factors that underlie these perceptions. Using semi-structured interviews, we found that some participants in Nima exhibited a generally positive sense of place attachment, while most of those from Old Fadama expressed predominantly negative feelings due to the severe deprivations they endure. This study underscores the need to view slums as complex and dynamic urban conditions rather than static and homogeneous environments. By taking a place-based approach, policymakers can better understand the unique needs and perspectives of slum residents, which is critical for developing effective interventions that promote positive place attachment and enhance the overall well-being of these communities.

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)0.002
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 score0.342
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.096
GPT teacher head0.354
Teacher spread0.259 · 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

Citations15
Published2023
Admission routes1
Has abstractyes

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