“The whole community is like a dumpsite … ” the political ecology of health in Harare’s informal settlements
Bibliographic record
Abstract
Using the Political Ecology of Health framework, this paper examines how socio-political and environmental processes intersect to shape health vulnerabilities and inequities in Hopley and Hatcliffe Extension, Harare’s largest informal settlements. Data were collected through four focus group discussions – two in each settlement – with eight participants each (n = 32). Data were thematically analysed using an inductive coding approach to identify patterns and narratives related to health vulnerability, spatial exclusion, and governance failures. The analysis shows that residents’ vulnerability to diseases and health risks in Hopley and Hatcliffe Extension is not merely the result of environmental exposure but is embedded in a broader context of colonial logics of exclusion, spatial marginalization, and infrastructural abandonment. The lived experiences of residents – ranging from reliance on contaminated shallow wells, makeshift pit latrines, and toxic urban ecologies to stigmatization in public health facilities – reveal how urban governance failures translate into everyday forms of harm and slow death. The paper makes a case for informal settlement upgrading interventions to build resilient and healthy urban environments.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".