Child Rights Education - Building Capabilities and Empowerment Through Social Constructivism
Bibliographic record
Abstract
Children have the right to a voice, to education and to education about their rights, as outlined in Article 12 and Article 29 of the United Nations Convention on the Rights of the Child. Child rights-based education can support children to be empowered with critical agency and exposed to connection to the wider world, better equipping them to become young global citizens and act in ways that demonstrate empathy and commitment to diversity, dignity, and equality. To obtain this goal, education systems must be aligned to foster these attributes and empower children to develop and exercise the capabilities that will best serve them in childhood as well as adulthood. This paper considers how we can support the empowerment and capabilities development of children through child rights-focused education using an integrated framework of empowerment, capabilities (Sen, 1999), and social constructivist (Vgotsky, 1978) education. Building on the foundations laid in the development and evolution of children’s rights, setting out the theoretical underpinnings and drawing on a case study of a rights-based education project, this paper will consider how rights-based education can be feasible and beneficial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.083 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".