Examining the impact of in-situ infrastructural upgrading on sustainability in informal settlements: The case of Accra, Ghana
Bibliographic record
Abstract
Many researchers advocate in-situ upgrading (providing local services and infrastructure) over relocation or resettlement for informal settlement intervention. However, the outcomes from the in-situ approach should be studied further, especially how they affect neighborhood sustainability. Toward that end, this paper investigates how the sustainability performance of settlements correlates with in-situ upgrading. Since Accra broadly employs in-situ upgrading to help underserved areas catch up, it serves as a helpful case study to identify how other African cities could evolve in the future. The findings show that in-situ infrastructural interventions will lead to better sustainability. Meanwhile, the satisfaction levels of infrastructure interventions are varied not only because of the different locations and stakeholders, but also due to their comprehensiveness and the timely upgrades undertaken for settlement expansion. This paper suggests in-situ upgrading is fundamental to Accra and many other African cities as it represents an essential guide to urban development and an opportunity for a “bottom-up” response to existing households.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".