Bibliographic record
Abstract
Harare, Zimbabwe’s capital city, has now joined the growing list of cities and ‘mega cities’ of the global South, which are now confronted by an ever-growing crisis precipitated by the deficient provision of basic services such as water and housing. Emblematic of these challenges are the cities of Lagos, Nairobi, Kumasi, Mumbai and Cairo. This article examines the mutation of Harare from what was once regarded as one of the most developed post-colonial cities in Africa dubbed the ‘sunshine city’ in local Zimbabwean parlance in the 1980s to a landscape of crisis and disease. The cholera outbreak in Harare towards the last quarter of 2008 extending into the first quarter of 2009 exposed the full magnitude of the city’s decrepit infrastructure. This pestilence laid bare the intricate political and municipal governance issues, the historical city–state tensions, and the impact of rapid urban population growth. Although the article focuses on the contemporary water crisis, it injects into the discourse a historical perspective in order to demonstrate that the recent set of factors which contributed to the occurrence of disease has profound structural origins dating back to the colonial days. The article, however, also emphasizes that postcolonial Harare’s dysfunctional water systems have been worsened by rapid urban population growth and repressive forms of political interventions in city governance.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".