Being Special: Nostalgia through Special Rates Areas and Community Improvement Districts in Cape Town Suburbs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".