Trapped or not trapped? An empirical investigation into the lived experiences of the urban poor in Harare’s selected informal settlements
Bibliographic record
Abstract
The role of informal settlements in human development remains contested in urban studies literature. For instance, some existing studies view urban informal settlements as hotspots of social unrest, squalor and precarious residential environments (poverty traps); while others perceive them as places where the poor become resourceful, ingenious, and develop necessary skills to navigate urban life (pathways out of poverty). The absence of systematic evidence on the nexus between informal settlements and human progress hinder sound urban policy practices. This paper examines the role of informal settlements in human development focusing on Hopley, Hatcliffe Extension and Epworth Ward 7–Harare’s three largest informal settlements. The study combines surveys, in-depth interviews, and focus group discussions with selected residents from the three neighborhoods. The study reveals that despite feeling ‘trapped’ in conditions of precarious, overcrowded, and insecure housing, coupled with discursive territorial stigmatization, some informal settlement residents are hopeful that their settlements will eventually improve. The ambivalence of Harare’s urban policy toward informal settlements must be replaced by a more positive approach to improve the livelihoods of people living in these neighborhoods.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".