He Ao Tūhono: A Comparative Look at Indigenous Early Learning Rights
Bibliographic record
Abstract
This article examines Indigenous children’s rights and early learning through a framework grounded in Te Tiriti o Waitangi, mātauranga Māori, and international Indigenous education policy. Drawing on the United Nations Convention on the Rights of the Child and the United Nations Declaration on the Rights of Indigenous Peoples, it argues that children’s rights cannot be meaningfully realised without Indigenous authority over language, culture, and education. Situated in Aotearoa New Zealand, the paper critiques the dominance of Western developmental models in early learning and positions Māori concepts of relationality and kaitiakitanga (guardianship) as foundational to rights-based pedagogy. Comparative examples from Hawai‘i, Canada, and Australia illustrate how Indigenous-led governance and language frameworks operationalise children’s rights through community authority rather than institutional inclusion. The article concludes that genuine transformation requires structural change beyond symbolic recognition, including shared governance with Indigenous communities, mandated professional learning in Indigenous pedagogies, and policy frameworks that centre Indigenous knowledge systems as foundational rather than supplementary.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".