Integrating indigenous knowledge and state-of-the-art Earth observation solutions for the Sendai framework implementation
Bibliographic record
Abstract
Purpose Indigenous peoples represent one of the most vulnerable groups and need access as well as hands-on experience in the use of emerging Earth observations (EO)-based DRR solutions at the community level, while balancing this learning with traditional indigenous knowledge (IK). However, complicating any engagement between EO and IK is the reality that IKs are diverse and dynamic, with location-specific relevance and accuracy. Additionally, the COVID-19 pandemic caused complex risks and cascading effects for which the world was not prepared. Thus, there is a need to examine the lessons learned and motivate emerging EO-based innovations and demonstrations related to DRR and climate change adaptation. Design/methodology/approach Hence, this study aims to undertake an in-depth assessment of IK related to DRR covering relevant UN instruments and provides state-of-the-art of opportunities presented by EO-based tools and solutions. Findings The overall research strategy was designed to integrate key components of IK for DRR in a coherent and logical way, with those offered by the EO technology developers and providers. There are several EO tools accessible that are relevant to integrate IK and complement DRR. The study examined and identified challenges and barriers to implement workable and replicable EO solutions in pursuit of resilience. Originality/value The key findings of this study will help create a balanced approach by acknowledging the importance of IK for DRR with co-development, co-creation and use of culturally relevant EO data and tools for sustainable innovation, capacity building and youth empowerment. The technological inequalities appear to be growing, and it would be challenging to meet the Sendai Framework indicators.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".