Disability and Children as Begging Guides: For how Long Shall Children be Used as Begging Guides by Visually Impaired Persons in Africa?
Bibliographic record
Abstract
This study examines the effect of using children as begging guides by people with sensory disabilities in Africa. It argues that in some African countries, visually impaired persons, especially parents and relatives, have devised the strategy of abusing their children or someone very close to them as begging guides for financial gain. While this strategy has remained a recurrent problem with severe social, economic, political, and legal implications, scientific research on addressing these implications is scarce. Given this, the article examines the rationale for using children as begging guides, its effects on the children and visually impaired parents, and mitigation strategies against abuse of children as begging guides in Africa. The qualitative research design based on key informant interviews (KIIs) complemented social exclusion and childhood theoretical underpinnings of the study. Results from (KIIs) showed that poverty, religion, cultural beliefs, financial profiteering, poor governance, breakdown in policy implementation against begging are among the rationale why children are abused and used as begging guides. The results also showed that these children experience low academic performance in school, harassment, psychological trauma, health complications from laborious trekking. At the same time, their visually impaired parents suffer from regrets and guilt of not being able to provide good parental care to their children. It concludes that good rehabilitation, vocational programmes, and prompt government supports for people with sensory disabilities would lessen the abuse and use of children as begging guides in Africa.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".