Able-bodied beggars on the street: perceived determinants of their prevalence and their targeted land uses in Ibadan metropolis, Nigeria
Bibliographic record
Abstract
Abstract This study examined the perceptions of able-bodied beggars to factors responsible for their prevalence and the land uses they occupied for their activities in Ibadan metropolis. Nigeria. Data were obtained from 117 (18, 12, 46, 13 and 28 in the areas of Sango, Iwo Road, Sabo, Challenge and Oja Oba, respectively) randomly selected able-bodied beggars using a structured questionnaire. The data were analyzed using percentages, mean index, standard deviation, chi-square and Analysis of Variance (ANOVA). The study showed that 51.3% of the beggars were males; the youngest was 19 years old, while the oldest was 59 years. The average age of the beggars was 33 years. Further findings revealed that the average household size for the study area was 5, while the standard deviation was 2.6. The important perceived factors responsible for the prevalence of begging among the able-bodied beggars, measured on a 5-point Likert Scale, were homelessness (4.29), lack of skills for gainful employment (3.77), debt (3.64), meeting cost of education/children’s education (3.64), inability to secure a job (3.52) and lack of food (2.97). The study further revealed that the prevalence of begging among able-bodied beggars varied with land uses. Against this background, it was concluded that the information obtained on the socio-economic attributes of able-bodied beggars, perceived determinants of their prevalence and their targeted land uses could be utilized by policy-makers and urban planners to proffer lasting solutions to the menace of begging.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".