Informality and the politics of urban flood management
Bibliographic record
Abstract
This paper explores reasons for unproductive urban flood management agendas in informal settlements. Does geography of informal settlements inform city-led flood management agendas? And in what ways have residents of informal settlements responded to city-led flood management approaches? The paper argues that the supposed city managers – both state institutions and professional bodies – have consistently acted in their own interest while successfully using ‘blame game’ to alienate their responsibility of successfully implementing flood management agendas in informal settlements. Using Accra (Ghana) as a case study, the study used multiple qualitative methods such as interviews, focus group discussion and secondary data analysis. Findings indicate that, overall, residents of informal settlements are gradually embracing the reality that city managers do not promote their interests in addressing perennial flood events. In turn, the flood management outcomes that policies and plans ostensibly seek to achieve have only been modestly realised. Instead, flood management agendas have had perverse implications for residents of informal settlements. Recommendations to improve the situation are proffered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.006 |
| 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".