Child Street - Trading Activities and Its Effect on the Educational Attainment of Its Victims in Epe Local Government Area of Lagos State
Bibliographic record
Abstract
This study examined child street trading activities and its effect on the educational attainment of its victims inEpe local government area of Lagos State. One hundred and twenty (120) respondents were selected from 6communities using purposive sampling techniques, administered by means of interview guide. Childreninterviewed were between 10 and 18 years of age. Descriptive statistics and inferential statistics were used indata analysis. The study revealed that most (60.8%) of the children who engaged in trading are females while39.2% were males. Also, 36.7% of the respondents are Christians while 40.3% are Muslims. Most (31.7%) of therespondents have a household size of 9 -12 persons while 34.2% have father’s occupation as fishing. Also,45.8% have mother’s occupation as trading. Only 20.0% undertake load carrying operation while 29.2% citedreason for involvement in street trading as poverty. Most (40.8%) are into sales of pure water. Majority of themearn a daily income of N500 – N1000 while 36.7% work morning and afternoon. Nevertheless, 70.9% of therespondents are of the opinion that child trading activities have a negative effect on the reading schedule ofchildren while 79.2% believes trading activities affect their school attendance rate. There is a significantrelationship between daily income and pure water selling (?2= 22.22, p < 0.05), orange hawking and head carrier(?2 = 21.72) p < 0.01). The study suggests the need for government to design appropriate programme aimed atpoverty reduction and recommends mass enlightenment for the populace to reduce the menace.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".