Harnessing Indigenous Knowledge for Education for Sustainable Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In an era of environmental and social crises, integrating Indigenous knowledge into Education for Sustainable Development is critical for fostering a holistic, inclusive and effective approach to sustainability. This article explores the essential role of Indigenous knowledge in contextualizing education, highlighting its contributions to sustainable resource management, environmental stewardship and cultural preservation. Historically marginalized, Indigenous wisdom offers practical solutions for contemporary global challenges, such as climate change and biodiversity loss. By valuing and incorporating Indigenous perspectives into educational frameworks, education stakeholders can create more robust and culturally relevant learning experiences that empower communities and promote social equity. Examples from various countries, including Zimbabwe, the Philippines, Peru and Canada, illustrate the transformative impact of blending Indigenous and scientific knowledge. The article argues for a paradigm shift in education, emphasizing the importance of respecting and utilizing Indigenous knowledge to achieve a more sustainable and just world for future generations.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it