Eliminating and Sustaining Factors Influencing Child Begging in North Central Geo-Political Zone of Nigeria
Bibliographic record
Abstract
The study examined eliminating and sustaining factors influencing child begging in North central geo-political zone of Nigeria.The researchers adopted survey research design with a population of 7,050 comprising children, mualluns and parents.Three research questions were used as a guide to the study and the sample of 721 comprising 621 mualluns and parents and 100 almajiri drawn from two sampled states through simple random and purposive sampling techniques.The instruments used for data collection were researchers developed questionnaire titled "Eliminating and sustaining factors of Child Begging Questionnaire (ESFCQ), interview and close observation which were face validated by three experts in Childhood Education, Islamic studies and Measurement and Evaluation from Federal College of Education, Kontagora, Niger State.The reliability estimate of ESFCQ was established using Cronbach Alpha's co-efficient method and the index ranged from 0.78 to 0.71.Findings of the study revealed that poverty, migration and almajiri syndrome are the major sustaining factors, while provision of adequate social amenities like schools, daily school feeding for pupils and reorientation can play a vital role as eliminating factors of child begging.The study therefore recommended that Government should improve the living condition of families to take care of their children and also restructure the Islamiyya to include modern subjects in the centers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".