To leave or not to leave? An analysis of individual and neighbourhood characteristics shaping place attachment in Harare's selected informal settlements
Bibliographic record
Abstract
Place attachment is one of the important characteristics of sustainable neighbourhoods. The dynamics of place attachment in deprived neighbourhoods remain under‐researched, especially in Global South contexts. This paper examines how individual socio‐demographic and neighbourhood characteristics influence place attachment in Harare's selected informal settlements, namely Hopley, Hatcliffe Extension, and Epworth Ward 7. These neighbourhoods were purposefully selected as Harare's largest informal settlements. The paper uses survey data collected from randomly sampled participants from the three neighbourhoods. These data were analyzed using binary logistic regression. Based on multivariate analysis, long‐time residents were 2.35 times more likely (OR = 2.35, p < 0.01) to report high place attachment, when compared to newcomers. When compared to renters, owner‐occupiers (OR = 2.91, p < 0.001) had higher odds of reporting high place attachment. Participants with savings were more likely (OR = 1.80, p < 0.05) to report high place attachment when compared with those who do not have savings. Neighbourhood reputation and neighbourhood safety positively influence place attachment in Harare's selected informal settlements. Surprisingly, those living in Epworth Ward 7 (OR = 0.48, p < 0.05) were less likely to report high place attachment. Nonetheless, this study demonstrates that residents of deprived neighbourhoods can develop high place attachment with their residential 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".