Learnings from the <i>#IndigenousESD</i> Global Research: Twenty-First Century Competencies for All Learners
Bibliographic record
Abstract
Abstract The 2030 Agenda for Sustainable Development promotes with the Sustainable Development Goal 4 a quality education for all and aims to ensure equal access to all levels of education and vocational training for vulnerable groups, such as Indigenous Peoples. However, most education systems are not yet in a position to embrace a culturally appropriate way of teaching children and youth of their Indigenous communities. The #IndigenousESD research creates a voice for relevant education stakeholder groups, including Indigenous Elders/leaders, ministry officials, parents, students, and teachers from communities with Indigenous students on their perceptions of quality education. Based on a participatory research approach developed together with Indigenous communities and researchers from around the world, dialogues held in 54 research settings in 26 countries show a focus on the acquisition of twenty-first century competencies for learners amongst the most important aspects of a quality education. For this article, the authors focused on knowledge, attitudes and skills, providing recommendations for policy makers in education to better address the needs and priorities of Indigenous communities. Findings from the research indicate that teaching twenty-first century competencies are at the center of concern in all stakeholder groups, yet want these competencies taught in a context to which Indigenous students can readily relate. Adjusting the pedagogy of delivering these common competencies in the classroom could be an important step towards a feasible and affordable path within existing education systems to better serve Indigenous students and all learners.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".