Children’s Rights in the Asia-Pacific Region: Critical Reflections on Participation, Education, Girls’ Rights and Child Marriage
Bibliographic record
Abstract
The realisation of children’s rights is a pressing global concern, with millions of children globally experiencing abuse, neglect, exploitation, and discrimination. The United Nations Committee on the Rights of the Child urges states to respect, promote and fulfil children’s rights in accordance with the Convention on the Rights of the Child. This paper details insights shared by leading children’s rights advocates at LAWASIA’s webinar, ‘Children and Young People’s Human Rights in Today’s World.’ In this webinar Dr Holly Doel-Mackaway (Children’s Rights Academic and Expert Counsellor, Human Rights Committee, LAWASIA) spoke with three leading children’s rights experts Mikiko Otani (Chair of the Committee on the Rights of the Child), Sharmila Sekaran (Co-Founder of Voice of the Children, Malaysia) and Rukmini Banerji (Chief Executive Officer of Pratham Education Foundation, India) about the status of children’s and young people’s human rights across the world today. Speakers identified key children’s rights challenges facing the Asia-Pacific region including grave violations of girls’ rights, the impacts of growing inequality and poverty on children, children’s participation, child marriage and other forms of abuse and exploitation, education equity and climate justice.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.045 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.017 |
| 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".