THE ROLE OF CITIES IN ENDING VIOLENCE AGAINST CHILDREN IN SOUTH AFRICA
Bibliographic record
Abstract
The global development agenda acknowledges the role of cities in achieving the United Nations’ Sustainable Development Goals (SDGs) and addressing contemporary challenges caused by urbanization. SDG 11 aspires to make “cities inclusive, safe, resilient and sustainable” by 2030, even as the global urban population continues to grow exponentially, along with — even more rapidly — the population of children living in cities. Cities are the level of government closest to people’s daily lives, and are best placed to address the numerous challenges and rights violations that children are exposed to, including sexual exploitation and abuse, violence, trafficking, and child labour. SDG 16.2 has the primary aim of ending the “abuse, exploitation, trafficking and all forms of violence against children”. Through the lens of the subsidiarity principle, this article argues that localization to the city level of law and policy strategies that address violence against children can provide normative and powerful legal tools for their protection. Although there is developing scholarly literature on the global aspirations expressed in SDG 11 and SDG 16.2, little has been offered from a child rights perspective on the role of city governments in the prevention of, and protection of children from, violence.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".