Reflections of Good Practice of Infusing ESD to Improve Education Outcomes for Indigenous Learners in Light of a Global Pandemic
Bibliographic record
Abstract
Abstract The COVID-19 global pandemic presented an unprecedented challenge to the sustainability strategies and initiatives of many nations. In many countries, education strategies and funding were negatively impacted and, consequently, especially vulnerable groups were highly affected, amongst them Indigenous communities around the world. As Indigenous communities were already amongst the most vulnerable before 2020, a strategically and well-planned recovery from this pandemic would be vital to secure their well-being. This article offers reflections on the potential of infusing Education for Sustainable Development (ESD) in the classroom, the school and the community as a whole, to deal with known and yet unprecedented sustainability challenges in presenting commonalities of 32 good practice reports from 21 countries collected in advance and during the global pandemic. Authors make the point of considering the pandemic and its widespread impact as yet another sustainability challenge and position ESD as a potential tool to achieve quality education and unleash the full potential of education for society when planning recovery efforts in hope for a better future of Indigenous communities in the long term. As the good practices were also included in a report of the UN Special Rapporteur on the Rights of Indigenous Peoples to the 48 th Session of the United Nations Human Rights Council , focusing on the post-pandemic recovery efforts for Indigenous Peoples, further thoughts on both official reports and their alignment with the overall 2030 Agenda from an ESD perspective are included.
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 | 0.161 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.043 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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".