Children in detention amidst COVID-19 in Africa: A wound untreated
Bibliographic record
Abstract
Children in detention in Africa are part of the vulnerable population exposed to the COVID-19 pandemic due to factors such as overcrowding, poor healthcare of inmates, and lousy state of the facilities. The number of children in detention was estimated to be about one million in 2010, and this has further increased threat to global health. The fall in operating criminal justice systems, from the aspect of rehabilitation and reform in Africa, to its being plagued with crisis, overpopulation, and inability to conform to fundamental human rights and health standards. It was noted that children in detention in Africa end up in prison mainly because they are either given birth to by incarcerated mothers or sentenced to jail based on their alleged criminal activity. Also, certain limitations in some African countries to track the prevalence of COVID-19 and other diseases include inaccessible data, non-specificity of data, and unreliable information regarding the current prison situation. Sometimes, these data could be insufficient and hard to comprehend, primarily if written in the local language. The efforts to resolve the untreated wounds of children in detention during COVID-19 are somewhat tricky. However, this paper identifies these limitations and proffers recommendations such as; the identification and implementation of strategies that support the continuity of child-centered services, prioritizing children for immediate release, and ensuring adequate protection of their health and well-being, among others.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".