Effects of Seasonal Variation on Informal Waste Collection in Ibadan, South-west Nigeria
Bibliographic record
Abstract
Despite the active participation of informal waste collectors (IWCs) in waste management in Ibadan, south-west Nigeria, a major observed challenge to effective operation of this group of workers is the variation in the seasons of the year and their accompanying weather futures. This study investigated the effects of seasonal changes on the types and volume of waste handled by the informal waste collectors, level of patronage and income earned in the five municipal local government areas of Ibadan. A cross-sectional survey approach was adopted and both primary and secondary data were sourced. Through questionnaire survey and field observations, data were collected from 253 informal waste collectors operating in the study area. Descriptive statistics (frequencies and percentages) and inferential statistics (ANOVA) were used in analysing the data obtained from the field work. The study established that the types and volume of waste collected and income earned by the informal waste collectors varied from season to season. Patronage of the informal waste collectors was found to be reduced by about 25% in the dry season owing to less volume of waste generated and increased burning. The low patronage reduced the income by about 25% on average. The implications of this are that the job security of IWCs is threatened and increased burning of waste increases the atmospheric carbon content, which depletes the ozone layer and consequently results in global warming. The study, therefore, recommended financial and technical assistance to the waste collectors by either government or non-governmental organisations to establish small waste merchandising business to cater for the period of low patronage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".