Flooding and Waste Disposal Practices of Urban Residents in Nigeria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rising incidence of flooding is a cause for global concern. Flooding is caused by both natural and human factors. In Nigeria, flooding has been attributed chiefly to human factors, such as poor waste disposal practices and management. Despite this known link, no empirical study is known to have engaged with urban residents to understand their actual waste disposal practices and ascertain their knowledge of the connection of their waste disposal practices to the flooding they are increasingly experiencing. This work fills this gap via an in-depth engagement with residents and experts on their waste disposal practices in the flood-prone city of Port Harcourt via a mixed-methods case study. Questionnaire surveys and qualitative interviews served as the primary data collection tools. The study confirms the poor waste practices of residents and provides empirical data on the prevalence of various forms of waste disposal practices. This provides key information that can guide the needed change in waste practices to eliminate this known flood driver in the pursuit of sustainable flood risk management. This is pertinent as waste management is one of the areas where citizens have agency to act. A behavioural shift is needed in this regard and must be encouraged via targeted public sensitization. Having local vanguards champion waste management behavioural turn is also recommended. The relevant authorities are encouraged to adopt a more sustainable approach to waste management by ensuring there are waste services and putting in place adequate disincentives to deter offenders.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it