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Record W2896838979 · doi:10.15273/jue.v8i2.8684

Being Special: Nostalgia through Special Rates Areas and Community Improvement Districts in Cape Town Suburbs

2018· article· en· W2896838979 on OpenAlexvenueno aff
Zarreen Kamalie

Bibliographic record

VenueJournal for Undergraduate Ethnography · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSouth African History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsCapeSociologyWhite (mutation)GeographyGender studiesHistoryCriminologyArchaeology

Abstract

fetched live from OpenAlex

This paper explores how memories and nostalgia inform the rationale of implementing Community Improvement Districts (CIDs) or Special Rates Areas (SRAs) as a means of crime prevention and urban maintenance in two formerly ‘whites-only’ Cape Town suburbs; Rondebosch and Mowbray. Through an exploration of the remembering, the maintenance and the resuscitation of an idealized past in a suburb that remains predominantly white after years of racial and economic exclusion, this paper interrogates the role of long-term resident nostalgia in post-apartheid South Africa in maintaining spatial apartheid. Using Svetlana Boym’s (2001) framework of nostalgia, particularly ‘restorative nostalgia’ and ‘reflective nostalgia,’ to interpret the memories of residents interviewed, this paper argues that it is nostalgia for an idealized past and a remembered specialness that sustains mentalities that give rise to spatially exclusive SRAs and CIDs. In this paper, public and social media discourse analysis and resident interviews allow us to understand residents’ memories and discussions around crime and urban degeneration and homelessness in Rondebosch. The purpose of this paper is to contribute to questions about spatial exclusivity in residential spaces in the post-apartheid era, particularly in a city that retains the legacy of spatial apartheid.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0000.001
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 head0.042
GPT teacher head0.324
Teacher spread0.282 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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