Participatory, Multimodal Approaches to Child Rights Education in Global Contexts: Reflections on a Study with Schoolchildren in Uganda and Canada
Bibliographic record
Abstract
Globally, we have much to learn about fulfilling international child education rights, particularly in times of crisis, as evidenced during the global COVID-19 pandemic. Although the right of children to know their rights is enshrined in the United Nations Convention on the Rights of the Child, and other documents, such as the African Charter on Rights and Welfare of the Child, child rights are rarely introduced to children as part of their formal learning experience in school, because children are deemed unable to understand the concepts of rights (and responsibilities) (Alderson, 2008; Jerome, 2018). This lack of child-rights education means children are denied opportunities for empowerment: e.g., awareness and knowledge needed for self-advocacy, advocacy for other children with respect to the ability to claim and exercise their rights (Covell et al., 2017; Wabwile, 2016). Drawing on a case study conducted in Uganda and Canada, this paper discusses how participatory, empowering, multimodal, and contextually-responsive/sensitive approaches to child rights education enables children to engage meaningfully in learning about their rights.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.069 | 0.035 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| 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".